Search results for: malignant tumor detection

Authors: Yang Zhou, Kangfeng Zheng, Wei Ni, Ren Ping Liu

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Software-defined networking (SDN) provides a solution for scalable network framework with decoupled control and data plane. However, this architecture also induces a particular distributed denial-of-service (DDoS) attack that can affect or even overwhelm the SDN network. DDoS attack detection problem has to date been mostly researched as entropy comparison problem. However, this problem lacks the utilization of SDN, and the results are not accurate. In this paper, we propose a DDoS attack detection method, which interprets DDoS detection as a signature matching problem and is formulated as Earth Mover’s Distance (EMD) model. Considering the feasibility and accuracy, we further propose to define the cost function of EMD to be a generalized Kullback-Leibler divergence. Simulation results show that our proposed method can detect DDoS attacks by comparing EMD values with the ones computed in the case without attacks. Moreover, our method can significantly increase the true positive rate of detection.

Keywords: DDoS detection, EMD, relative entropy, SDN

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Authors: Amjad Al Shammari, Ashraf Ibrahim, Laila Seada

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Background and Objectives: Incidence of thyroid carcinoma has been increasing world-wide. In the present study, we evaluated diagnostic accuracy of Fine needle aspiration (FNA) and its efficiency in early detecting neoplastic lesions of thyroid gland over a 3-year period. Methods: Data have been retrieved from pathology files in King Khalid Hospital. For each patient, age, gender, FNA, site & size of nodule and final histopathologic diagnosis were recorded. Results: Study included 490 cases where 419 of them were female and 71 male. Male to female ratio was 1:6. Mean age was 43 years for males and 38 for females. Cases with confirmed histopathology were 131. In 101/131 (77.1%), concordance was found between FNA and histology. In 30/131 (22.9%), there was discrepancy in diagnosis. Total malignant cases were 43, out of which 14 (32.5%) were true positive and 29 (67.44%) were false negative. No false positive cases could be found in our series. Conclusion: FNA could diagnose benign nodules in all cases, however, in malignant cases, ultrasound findings have to be taken into consideration to avoid missing of a microcarcinoma in the contralateral lobe.

Keywords: FNA, hail, histopathology, thyroid

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Authors: Emhimed Saffor

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In this paper, the problem of edge detection in digital images is considered. Three methods of edge detection based on mathematical morphology algorithm were applied on two sets (Brain and Chest) CT images. 3x3 filter for first method, 5x5 filter for second method and 7x7 filter for third method under MATLAB programming environment. The results of the above-mentioned methods are subjectively evaluated. The results show these methods are more efficient and satiable for medical images, and they can be used for different other applications.

Keywords: CT images, Matlab, medical images, edge detection

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Authors: Victoria Siriaki Jorry, I. S. Mbalawata, Hayong Shin

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The main objective in a change detection problem is to develop algorithms for efficient detection of gradual and/or abrupt changes in the parameter distribution of a process or time series data. In this paper, we present a modified cumulative (MCUSUM) algorithm to detect the start and end of a time-varying linear drift in mean value of a time series data based on likelihood ratio test procedure. The design, implementation and performance of the proposed algorithm for a linear drift detection is evaluated and compared to the existing CUSUM algorithm using different performance measures. An approach to accurately approximate the threshold of the MCUSUM is also provided. Performance of the MCUSUM for gradual change-point detection is compared to that of standard cumulative sum (CUSUM) control chart designed for abrupt shift detection using Monte Carlo Simulations. In terms of the expected time for detection, the MCUSUM procedure is found to have a better performance than a standard CUSUM chart for detection of the gradual change in mean. The algorithm is then applied and tested to a randomly generated time series data with a gradual linear trend in mean to demonstrate its usefulness.

Keywords: average run length, CUSUM control chart, gradual change detection, likelihood ratio test

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Authors: O. C. Abbah, O. Obidoa, J. Omale

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Prostate tumor is fast becoming a leading cause of morbidity and mortality in human male adults, with 50 percent of men aged 50 years and above having histological evidence of the benign tumor. The study was set out to undertake phytochemical screening and proximate analysis of the pulp of A. muricata fruit - soursop; to determine the acute toxicity of the fruit pulp extract and its effect on male albino Wistar rats with concurrent induction of experimental benign prostate hyperplasia (BPH). Eighteen rats (average weight of 100g) were used for the lethality studies and were orally administered graded doses of aqueous extracts of the fruit pulp up to 5000 mg/kg body weight. Twenty five rats weighing 150-200g were divided into five groups of five rats each for the tumor studies. The groups included four controls – Hormone control, HC, which took Testosterone, T; and Estradiol, E2 – only, in olive oil as vehicle; Vehicle control, VC; Soursop control, SC, which received the extract only; VS, Vehicle and Soursop – and the Test group, TG (500mg/kg b.w.). All rats were dosed orally. Tumor was induced with exogenous Testosterone propionate: Estradiol valerate at 300µg: 80µg/kg b.w. (respectively) in olive oil, administered subcutaneously in the inguinal region of the rats on alternate days for 21 days. Administration of the fruit pulp at graded doses up to 5000mg/kg resulted in no lethality even after 72 hours. Results from tumor studies revealed that the administration of the fruit extracts significantly (p < 0.05) reduced the relative prostate weight of the TG compared with the HC, with values of 006±0.001 and 0.010±0.003 respectively. Treatment with vehicle, soursop and vehicle with soursop caused no significant (p>0.05) change in prostate size, with their respective relative prostate weights being 0.002±0.001, 0.004±0.002 and 0.002±0.001 compared with TG. Also, treatment with A. muricata fruit extract significantly decreased (p < 0.05) serum prostate specific antigen, PSA, in TG compared with HC, with values 0.055±0.017 and 0.194±0.068 ng/ml respectively. Furthermore, A. muricata administration displayed Testosterone boosting, Estradiol lowering and consequently testosterone-estradiol ratio increasing potential at the end of the 21 days. The preventive property of soursop against experimental BPH was corroborated by histological evidence in this study. The study concludes that A. muricata fruit holds a great potential for benign prostate tumor prevention and, possibly, management.

Keywords: annona muricata, benign prostate tumor, hormone, preventive potential, soursop

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Authors: Kaveh Shahi, Helmi Z. M. Shafri, Ebrahim Taherzadeh

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In remote sensing, shadow causes problems in many applications such as change detection and classification. It is caused by objects which are elevated, thus can directly affect the accuracy of information. For these reasons, it is very important to detect shadows particularly in urban high spatial resolution imagery which created a significant problem. This paper focuses on automatic shadow detection based on a new spectral index for multispectral imagery known as Shadow Detection Index (SDI). The new spectral index was tested on different areas of World-View 2 images and the results demonstrated that the new spectral index has a massive potential to extract shadows effectively and automatically.

Keywords: spectral index, shadow detection, remote sensing images, World-View 2

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Authors: S. Sudhakar, Geetha Manjunath, Siva Teja Kakileti, Himanshu Madhu

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Diagnosis of breast cancer at early stages has seen better clinical and survival outcomes. Survival rates in developing countries like India are very low due to accessibility and affordability issues of screening tests such as Mammography. In addition, Mammography is not much effective in younger women with dense breasts. This leaves a gap in current screening methods. Thermalytix is a new technique for detecting breast abnormality in a non-contact, non-invasive way. It is an AI-enabled computer-aided diagnosis solution that automates interpretation of high resolution thermal images and identifies potential malignant lesions. The solution is low cost, easy to use, portable and is effective in all age groups. This paper presents the results of a retrospective comparative analysis of Thermalytix over Mammography and Clinical Breast Examination for breast cancer screening. Thermalytix was found to have better sensitivity than both the tests, with good specificity as well. In addition, Thermalytix identified all malignant patients without palpable lumps.

Keywords: breast cancer screening, radiology, thermalytix, artificial intelligence, thermography

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Authors: Nam-Uk Kim, Sung-Hwan Kim, Tai-Myoung Chung

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Typical security management systems are not suitable for detecting APT attack, because they cannot draw the big picture from trivial events of security solutions. Although SIEM solutions have security analysis engine for that, their security analysis mechanisms need to be verified in academic field. Although this paper proposes merely an architectural model for APT detection, we will keep studying on correlation analysis mechanism in the future.

Keywords: advanced persistent threat, anomaly detection, data mining

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Authors: Soroosh Torabi, Brad Berron, Ren Xu, Christine Trinkle

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Microfluidics integrated with 3D cell culture is a powerful technology to mimic cellular environment, and can be used to study cell activities such as proliferation, migration and response to drugs. This technology has gained more attention in cancer studies over the past years, and many organ-on-a-chip systems have been developed to study cancer cell behaviors in an ex-vivo tumor microenvironment. However, there are still some barriers to adoption which include low throughput, complexity in 3D cell culture integration and limitations on non-optical analysis of cells. In this study, a user-friendly microfluidic multi-well plate was developed to mimic the in vivo tumor microenvironment. The microfluidic platform feeds multiple 3D cell culture sites at the same time which enhances the throughput of the system. The platform uses hydrophobic Cassie-Baxter surfaces created by microchannels to enable convenient loading of hydrogel/cell suspensions into the device, while providing barrier free placement of the hydrogel and cells adjacent to the fluidic path. The microchannels support convective flow and diffusion of nutrients to the cells and a removable lid is used to enable further chemical and physiological analysis on the cells. Different breast cancer cell lines were cultured in the device and then monitored to characterize nutrient delivery to the cells as well as cell invasion and proliferation. In addition, the drug response of breast cancer cell lines cultured in the device was compared to the response in xenograft models to the same drugs to analyze relevance of this platform for use in future drug-response studies.

Keywords: microfluidics, multi-well 3d cell culture, tumor microenvironment, tumor-on-a-chip

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Authors: Yeongyu Choi, Ju H. Park, Ho-Youl Jung

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In this paper, we propose labeling based RANSAC algorithm for lane detection. Advanced driver assistance systems (ADAS) have been widely researched to avoid unexpected accidents. Lane detection is a necessary system to assist keeping lane and lane departure prevention. The proposed vision based lane detection method applies Canny edge detection, inverse perspective mapping (IPM), K-means algorithm, mathematical morphology operations and 8 connected-component labeling. Next, random samples are selected from each labeling region for RANSAC. The sampling method selects the points of lane with a high probability. Finally, lane parameters of straight line or curve equations are estimated. Through the simulations tested on video recorded at daytime and nighttime, we show that the proposed method has better performance than the existing RANSAC algorithm in various environments.

Keywords: Canny edge detection, k-means algorithm, RANSAC, inverse perspective mapping

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Authors: Chro Kavian

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The studies conducted show that cancer is a disease caused by populations of microbes, a notion gaining traction as the interaction between the human microbiome and the tumor microenvironment (TME) increasingly shows how environment and microbes dictate the progress and treatment of neoplastic diseases. A person’s human microbiome is defined as a collection of bacteria, fungi, viruses, and other microorganisms whose structure and composition influence biological processes like immune system modulation and nutrient metabolism, which, in turn, affect how susceptible a person is to neoplastic diseases, and response to different therapies. Recent reports demonstrated the influence specific microbiome bacterial populations have on the TME, thereby altering tumoral behaviors and the TME’s contributing factors that impact patients' lives. In addition, gut microbes and their SCFA products are important determinants of the inflammatory landscape of tumors and augment anti-tumor immunity, which can influence immunotherapy outcomes. Studies have also found that dysbiosis, or microbial imbalance, correlates with biological processes such as cancer progression, metastasis, and therapy resistance, leading scientists to explore the use of microbiome deficiencies as adjunctive approaches to chemotherapy and other, more traditional treatments. Nonetheless, mental health practitioners struggling to comprehend the existent gap between cancer patients with pronounced resolutive capabilities and the profound clinical impact Microbiome-targeted cancer therapy has been proven to possess.

Keywords: microbiome, cancer, tumor, immune system

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Authors: Yihenew Simegniew Birhan, Hsieh Chih Tsai

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The recent advancements in synthetic chemistry and nanotechnology fostered the development of different nanocarriers for enhanced intracellular delivery of pharmaceutical agents to tumor cells. Polymeric micelles (PMs), characterized by small size, appreciable drug loading capacity (DLC), better accumulation in tumor tissue via enhanced permeability and retention (EPR) effect, and the ability to avoid detection and subsequent clearance by the mononuclear phagocyte (MNP) system, are convenient to improve the poor solubility, slow absorption and non-selective biodistribution of payloads embedded in their hydrophobic cores and hence, enhance the therapeutic efficacy of chemotherapeutic agents. Recently, redox-responsive polymeric micelles have gained significant attention for the delivery and controlled release of anticancer drugs in tumor cells. In this study, we synthesized redox-responsive diselenide bond containing amphiphilic polymer, Bi(mPEG-PLGA)-Se₂ from mPEG-PLGA, and 3,3'-diselanediyldipropanoic acid (DSeDPA) using DCC/DMAP as coupling agents. The successful synthesis of the copolymers was verified by different spectroscopic techniques. Above the critical micelle concentration, the amphiphilic copolymer, Bi(mPEG-PLGA)-Se₂, self-assembled into stable micelles. The DLS data indicated that the hydrodynamic diameter of the micelles (123.9 ± 0.85 nm) was suitable for extravasation into the tumor cells through the EPR effect. The drug loading content (DLC) and encapsulation efficiency (EE) of DOX-loaded micelles were found to be 6.61 wt% and 54.9%, respectively. The DOX-loaded micelles showed initial burst release accompanied by sustained release trend where 73.94% and 69.54% of encapsulated DOX was released upon treatment with 6mM GSH and 0.1% H₂O₂, respectively. The biocompatible nature of Bi(mPEG-PLGA)-Se₂ copolymer was confirmed by the cell viability study. In addition, the DOX-loaded micelles exhibited significant inhibition against HeLa cells (44.46%), at a maximum dose of 7.5 µg/mL. The fluorescent microscope images of HeLa cells treated with 3 µg/mL (equivalent DOX concentration) revealed efficient internalization and accumulation of DOX-loaded Bi(mPEG-PLGA)-Se₂ micelles in the cytosol of cancer cells. In conclusion, the intelligent, biocompatible, and the redox stimuli-responsive behavior of Bi(mPEG-PLGA)-Se₂ copolymer marked the potential applications of diselenide-linked mPEG-PLGA micelles for the delivery and on-demand release of chemotherapeutic agents in cancer cells.

Keywords: anticancer drug delivery, diselenide bond, polymeric micelles, redox-responsive

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Authors: Mohammad Hossein Habibi, Atena Sadat Hosseini

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Colorectal cancer is the third leading cause of cancer-related death in the world. Colorectal cancer screening, early detection, and treatment programs could benefit from the most up-to-date information on the disease's burden, given the present worldwide trend of increasing colorectal cancer incidence. Tumor recurrence and resistance are exacerbated by the presence of chemotherapy-resistant cancer stem cells that can generate rapidly proliferating tumor cells. In addition, tumor cells can evolve chemoresistance through adaptation mechanisms. In this work, we used in silico analysis to select suitable GEO datasets. In this study, we compared slow-growing cancer stem cells with high-growth colorectal cancer-derived cancer stem cells. We then evaluated the signal pathways, transcription factors, and kinases associated with these two types of cancer stem cells. A total of 980 upregulated genes and 870 downregulated genes were clustered. MAPK signaling pathway, AGE-RAGE signaling pathway in diabetic complications, Fc gamma R-mediated phagocytosis, and Steroid biosynthesis signaling pathways were observed in upregulated genes. Also, caffeine metabolism, amino sugar and nucleotide sugar metabolism, TNF signaling pathway, and cytosolic DNA-sensing pathway were involved in downregulated genes. In the next step, we evaluated the best transcription factors and kinases in two types of cancer stem cells. In this regard, NR2F2, ZEB2, HEY1, and HDGF as transcription factors and PRDM5, SMAD, CBP, and KDM2B as critical kinases in upregulated genes. On the other hand, IRF1, SPDEF, NCOA1, and STAT1 transcription factors and CTNNB1 and CDH7 kinases were regulated low expression genes. Using bioinformatics analysis in the present study, we conducted an in-depth study of colorectal cancer stem cells at low and high growth rates so that we could take further steps to detect and even target these cells. Naturally, more additional tests are needed in this direction.

Keywords: colorectal cancer, bioinformatics analysis, transcription factor, kinases, cancer stem cells

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Authors: Mouna Zitouni, Mariem Tounsi

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This research introduces an application of the Riesz mixture model for medical image segmentation for accurate diagnosis and treatment of brain tumors. We propose a pixel classification technique based on the Riesz distribution, derived from an extended Bartlett decomposition. To our knowledge, this is the first study addressing this approach. The Expectation-Maximization algorithm is implemented for parameter estimation. A comparative analysis, using both synthetic and real brain images, demonstrates the superiority of the Riesz model over a recent method based on the Wishart distribution.

Keywords: EM algorithm, segmentation, Riesz probability distribution, Wishart probability distribution

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Authors: Gherbi Nabil

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Detection of ground targets in SAR radar images is an important area for radar information processing. In the literature, various algorithms have been discussed in this context. However, most of them are of low robustness and accuracy. To this end, we discuss target detection in SAR images based on compressive sensing. Firstly, traditional SAR image target detection algorithms are discussed, and their limitations are highlighted. Secondly, a compressive sensing method is proposed based on the sparsity of SAR images. Next, the detection problem is solved using Multiple Measurements Vector configuration. Furthermore, a robust Alternating Direction Method of Multipliers (ADMM) is developed to solve the optimization problem. Finally, the detection results obtained using raw complex data are presented. Experimental results on real SAR images have verified the effectiveness of the proposed algorithm.

Keywords: compressive sensing, raw complex data, synthetic aperture radar, ADMM

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Authors: Yuxiang Pan, Yong Qiu, Chenlei Gu, Ping Wang

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In the past decade, three-dimensional (3D) tumor cell models have attracted increasing interest in the field of drug screening due to their great advantages in simulating more accurately the heterogeneous tumor behavior in vivo. Drug sensitivity testing based on 3D tumor cell models can provide more reliable in vivo efficacy prediction. The gold standard fluorescence staining is hard to achieve the real-time and label-free monitoring of the viability of 3D tumor cell models. In this study, micro-groove impedance sensor (MGIS) was specially developed for dynamic and non-invasive monitoring of 3D cell viability. 3D tumor cells were trapped in the micro-grooves with opposite gold electrodes for the in-situ impedance measurement. The change of live cell number would cause inversely proportional change to the impedance magnitude of the entire cell/matrigel to construct and reflect the proliferation and apoptosis of 3D cells. It was confirmed that 3D cell viability detected by the MGIS platform is highly consistent with the standard live/dead staining. Furthermore, the accuracy of MGIS platform was demonstrated quantitatively using 3D lung cancer model and sophisticated drug sensitivity testing. In addition, the parameters of micro-groove impedance chip processing and measurement experiments were optimized in details. The results demonstrated that the MGIS and 3D cell-based biosensor and would be a promising platform to improve the efficiency and accuracy of cell-based anti-cancer drug screening in vitro.

Keywords: micro-groove impedance sensor, 3D cell-based biosensors, 3D cell viability, micro-electromechanical systems

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Authors: Varun Agarwal

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Introduction: With the advent of personalized medicine, histopathological review of whole slide images (WSIs) for cancer diagnosis presents an exceedingly time-consuming, complex task. Specifically, detecting metastatic regions in WSIs of sentinel lymph node biopsies necessitates a full-scanned, holistic evaluation of the image. Thus, digital pathology, low-level image manipulation algorithms, and machine learning provide significant advancements in improving the efficiency and accuracy of WSI analysis. Using Camelyon16 data, this paper proposes a deep learning pipeline to automate and ameliorate breast cancer metastasis localization and WSI classification. Methodology: The model broadly follows five stages -region of interest detection, WSI partitioning into image tiles, convolutional neural network (CNN) image-segment classifications, probabilistic mapping of tumor localizations, and further processing for whole WSI classification. Transfer learning is applied to the task, with the implementation of Inception-ResNetV2 - an effective CNN classifier that uses residual connections to enhance feature representation, adding convolved outputs in the inception unit to the proceeding input data. Moreover, in order to augment the performance of the transfer learning CNN, a stack of convolutional denoising autoencoders (CDAE) is applied to produce embeddings that enrich image representation. Through a saliency-detection algorithm, visual training segments are generated, which are then processed through a denoising autoencoder -primarily consisting of convolutional, leaky rectified linear unit, and batch normalization layers- and subsequently a contrast-normalization function. A spatial pyramid pooling algorithm extracts the key features from the processed image, creating a viable feature map for the CNN that minimizes spatial resolution and noise. Results and Conclusion: The simplified and effective architecture of the fine-tuned transfer learning Inception-ResNetV2 network enhanced with the CDAE stack yields state of the art performance in WSI classification and tumor localization, achieving AUC scores of 0.947 and 0.753, respectively. The convolutional feature retention and compilation with the residual connections to inception units synergized with the input denoising algorithm enable the pipeline to serve as an effective, efficient tool in the histopathological review of WSIs.

Keywords: breast cancer, convolutional neural networks, metastasis mapping, whole slide images

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Authors: Vandana Singh, Subash Singh

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Introduction: Oral squamous cell carcinoma is the most common malignant tumor of the oral cavity. Normally the death of cell and the growth are active processes and depend not only on external factors but also on the expression of genes like Bcl-2, which activate and inhibit apoptosis. The term Bcl-2 is an acronym for B-cell lymphoma/ leukemia -2 genes. Objectives: An attempt was made to evaluate Bcl-2 oncoprotein expression in patients with oral precancer and cancer and to assess possible correlation between Bcl-2 oncoprotein expression and clinicopathological features of oral precancer and cancer. Material and Methods: This is a selective prospective clinical and immunohistochemical study. Clinicopathological examination is correlated with immunohistochemical findings. The immunolocalization of Bcl-2 protein is performed using the labeled streptavidin biotin (LSAB) method. To visualize the reaction, 3, 3-diaminobenzidine (DAB) is used. Results: Bcl-2 expression was positive in 11 [36.66 %, low Bcl-2 expression 3 (10.00 %), moderate Bcl-2 expression 7 (23.33 %), and high Bcl-2 expression 1 (3.33 %)] oral cancer cases and in 14 [87.50 %, low expression 8 (50 %), moderate expression 6 (37.50 %)] precancer cases. Conclusion: On the basis of the results of our study we conclude that positive Bcl-2 expression may be an indicator of poor prognosis in oral cancer and precancer. Relevance: It has been reported that there is deregulation of Bcl-2 expression during progression from oral epithelial dysplasia to squamous cell carcinoma. It can be used for revealing progression of epithelial dysplasia to malignancy and as a prognostic marker in oral precancer and cancer.

Keywords: BcL-2, immunohistochemistry, oral cancer, oral precancer

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Authors: Hyun-Koo Kim, Yong-Hun Kim, Soo-Young Suk, Ju H. Park, Ho-Youl Jung

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This paper presents an effective speed hump detection process at the day-time. we focus only on round types of speed humps in the day-time dynamic road environment. The proposed speed hump detection scheme consists mainly of two process as stereo matching and speed hump detection process. Our proposed process focuses to speed hump detection process. Speed hump detection process consist of noise reduction step, data fusion step, and speed hemp detection step. The proposed system is tested on Intel Core CPU with 2.80 GHz and 4 GB RAM tested in the urban road environments. The frame rate of test videos is 30 frames per second and the size of each frame of grabbed image sequences is 1280 pixels by 670 pixels. Using object-marked sequences acquired with an on-vehicle camera, we recorded speed humps and non-speed humps samples. Result of the tests, our proposed method can be applied in real-time systems by computation time is 13 ms. For instance; our proposed method reaches 96.1 %.

Keywords: data fusion, round types speed hump, speed hump detection, surface filter

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Authors: Kun Xu, Yuan Xu, Jia Qiao

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The document detection plays an important role in optical character recognition and text analysis. Because the traditional detection methods have weak generalization ability, and deep neural network has complex structure and large number of parameters, which cannot be well applied in mobile devices, this paper proposes a lightweight Document Corner Detection Network (DCDNet). DCDNet is a two-stage architecture. The first stage with Encoder-Decoder structure adopts depthwise separable convolution to greatly reduce the network parameters. After introducing the Feature Attention Union (FAU) module, the second stage enhances the feature information of spatial and channel dim and adaptively adjusts the size of receptive field to enhance the feature expression ability of the model. Aiming at solving the problem of the large difference in the number of pixel distribution between corner and non-corner, Weighted Binary Cross Entropy Loss (WBCE Loss) is proposed to define corner detection problem as a classification problem to make the training process more efficient. In order to make up for the lack of Dataset of document corner detection, a Dataset containing 6620 images named Document Corner Detection Dataset (DCDD) is made. Experimental results show that the proposed method can obtain fast, stable and accurate detection results on DCDD.

Keywords: document detection, corner detection, attention mechanism, lightweight

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Authors: Jiandong Lv, Xingang Wang, Cuiling Shao

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The rapid development of social media platforms has made it one of the important news sources. While it provides people with convenient real-time communication channels, fake news and rumors are also spread rapidly through social media platforms, misleading the public and even causing bad social impact in view of the slow speed and poor consistency of artificial rumor detection. We propose an end-to-end rumor detection model-TIMF, which captures the dependencies between multimodal data based on the interactive attention mechanism, uses a transformer for cross-modal feature sequence mapping and combines hybrid fusion strategies to obtain decision results. This paper verifies two multi-modal rumor detection datasets and proves the superior performance and early detection performance of the proposed model.

Keywords: hybrid fusion, multimodal fusion, rumor detection, social media, transformer

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Authors: Navid Mosallaei, Mahmoud Reza Jaafari, Mohammad Yahya Hanafi-Bojd, Shiva Golmohammadzadeh, Bizhan Malaekeh-Nikouei

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Background: Docetaxel (DTX), a potent anticancer drug derived from the European yew tree, is effective against various human cancers by inhibiting microtubule depolymerization. Solid lipid nanoparticles (SLNs) have gained attention as drug carriers for enhancing drug effectiveness and safety. SLNs, submicron-sized lipid-based particles, can passively target tumors through the "enhanced permeability and retention" (EPR) effect, providing stability, drug protection, and controlled release while being biocompatible. Methods: The SLN formulation included biodegradable lipids (Compritol and Precirol), hydrogenated soy phosphatidylcholine (H-SPC) as a lipophilic co-surfactant, and Poloxamer 188 as a non-ionic polymeric stabilizer. Two SLN preparation techniques, probe sonication and microemulsion, were assessed. Characterization encompassed SLNs' morphology, particle size, zeta potential, matrix, and encapsulation efficacy. In-vitro cytotoxicity and cellular uptake studies were conducted using mouse colorectal (C-26) and human malignant melanoma (A-375) cell lines, comparing SLN-DTX with Taxotere®. In-vivo studies evaluated tumor inhibitory efficacy and survival in mice with colorectal (C-26) tumors, comparing SLNDTX withTaxotere®. Results: SLN-DTX demonstrated stability, with an average size of 180 nm and a low polydispersity index (PDI) of 0.2 and encapsulation efficacy of 98.0 ± 0.1%. Differential scanning calorimetry (DSC) suggested amorphous encapsulation of DTX within SLNs. In vitro studies revealed that SLN-DTX exhibited nearly equivalent cytotoxicity to Taxotere®, depending on concentration and exposure time. Cellular uptake studies demonstrated superior intracellular DTX accumulation with SLN-DTX. In a C-26 mouse model, SLN-DTX at 10 mg/kg outperformed Taxotere® at 10 and 20 mg/kg, with no significant differences in body weight changes and a remarkably high survival rate of 60%. Conclusion: This study concludes that SLN-DTX, prepared using the probe sonication, offers stability and enhanced therapeutic effects. It displayed almost same in vitro cytotoxicity to Taxotere® but showed superior cellular uptake. In a mouse model, SLN-DTX effectively inhibited tumor growth, with 10 mg/kg outperforming even 20 mg/kg of Taxotere®, without adverse body weight changes and with higher survival rates. This suggests that SLN-DTX has the potential to reduce adverse effects while maintaining or enhancing docetaxel's therapeutic profile, making it a promising drug delivery strategy suitable for industrialization.

Keywords: docetaxel, Taxotere®, solid lipid nanoparticles, enhanced permeability and retention effect, drug delivery, cancer chemotherapy, cytotoxicity, cellular uptake, tumor inhibition

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Authors: Jingting Luo, Yong Wang, Ying Wang

Abstract:

Pedestrian detection in image or video data is a very important and challenging task in security surveillance. The difficulty of this task is to locate and detect pedestrians of different scales in complex scenes accurately. To solve these problems, a deep neural network (RT-YOLOv3) is proposed to realize real-time pedestrian detection at different scales in security monitoring. RT-YOLOv3 improves the traditional YOLOv3 algorithm. Firstly, the deep residual network is added to extract vehicle features. Then six convolutional neural networks with different scales are designed and fused with the corresponding scale feature maps in the residual network to form the final feature pyramid to perform pedestrian detection tasks. This method can better characterize pedestrians. In order to further improve the accuracy and generalization ability of the model, a hybrid pedestrian data set training method is used to extract pedestrian data from the VOC data set and train with the INRIA pedestrian data set. Experiments show that the proposed RT-YOLOv3 method achieves 93.57% accuracy of mAP (mean average precision) and 46.52f/s (number of frames per second). In terms of accuracy, RT-YOLOv3 performs better than Fast R-CNN, Faster R-CNN, YOLO, SSD, YOLOv2, and YOLOv3. This method reduces the missed detection rate and false detection rate, improves the positioning accuracy, and meets the requirements of real-time detection of pedestrian objects.

Keywords: pedestrian detection, feature detection, convolutional neural network, real-time detection, YOLOv3

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Authors: J. R. Ashrapov, G. A. Alihodzhaeva, D. E. Abdullaev, N. R. Kadirbekov

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Diagnosis of brain tumors is one of the challenges, as several central nervous system diseases run the same symptoms. Modern diagnostic techniques such as CT, MRI helps to significantly improve the surgery in the operating period, after surgery, after allowing time to identify postoperative complications in neurosurgery. Purpose: To study the MRI characteristics and localization of brain tumors in children and to detect the postoperative complications in the postoperative period. Materials and methods: A retrospective study of treatment of 62 children with brain tumors in age from 2 to 5 years was performed. Results of the review: MRI scan of the brain of the 62 patients 52 (83.8%) case revealed a brain tumor. Distribution on MRI of brain tumors found in 15 (24.1%) - glioblastomas, 21 (33.8%) - astrocytomas, 7 (11.2%) - medulloblastomas, 9 (14.5%) - a tumor origin (craniopharyngiomas, chordoma of the skull base). MRI revealed the following characteristic features: an additional sign of the heterogeneous MRI signal of hyper and hypointensive T1 and T2 modes with a different perifocal swelling degree with involvement in the process of brain vessels. The main objectives of postoperative MRI study are the identification of early or late postoperative complications, evaluation of radical surgery, the identification of the extended-growing tumor that (in terms of 3-4 weeks). MRI performed in the following cases: 1. Suspicion of a hematoma (3 days or more) 2. Suspicion continued tumor growth (in terms of 3-4 weeks). Conclusions: Magnetic resonance tomography is a highly informative method of diagnostics of brain tumors in children. MRI also helps to determine the effectiveness and tactics of treatment and the follow up in the postoperative period.

Keywords: brain tumors, children, MRI, treatment

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Authors: Hamidreza Jabal Ameli, Anahita Movahedi

Abstract:

Purpose: In radionuclide therapy, radioactive atoms are coupled to monoclonal antibodies (mAbs) for treating cancer tumor while limiting radiation to healthy tissues. We know that tumoral and normal tissues are not equally sensitive to radiation. In fact, biological effects such as cellular repair processes or the presence of less radiosensitive cells such as hypoxic cells should be taken account. For this reason, in this paper, we want to calculate biological effect dose (BED) inside tumoral area and healthy cells around tumors. Methods: In this study, deposited doses of a radionuclide, gold-198, inside cells lattice and surrounding healthy tissues were calculated with Monte Carlo method. The elemental compositions and density of malignant and healthy tissues were obtained from ICRU Report 44. For reaching to real condition of oxygen effects, the necrosis and hypoxia area inside tumors has been assessed. Results: With regard to linear-quadratic expression which was defined in Monte Carlo, results showed that a large amount of BED is deposited in the well-oxygenated part of the hypoxia area compared to necrosis area. Moreover, there is a significant difference between the curves of absorbed dose with BED and without BED.

Keywords: biological dose, monte carlo, hypoxia, radionuclide therapy

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Authors: Maher un Nisa, Ahsan Khawaja

Abstract:

Retinal imaging with Ultra-WideField (UWF) view technology has opened up new avenues in the field of retinal pathology detection. Recent developments in retinal imaging such as Optos California Imaging Device helps in acquiring high resolution images of the retina to help the Ophthalmologists in diagnosing and analyzing eye related pathologies more accurately. This paper investigates the acquired retinal details by comparing vessel detection in standard 450 color fundus images with the state of the art 2000 UWF retinal images.

Keywords: color fundus, retinal images, ultra-widefield, vessel detection

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Authors: Sergei Aleinik, Yuri Matveev

Abstract:

In this paper a novel method for the detection of clipping in speech signals is described. It is shown that the new method has better performance than known clipping detection methods, is easy to implement, and is robust to changes in signal amplitude, size of data, etc. Statistical simulation results are presented.

Keywords: clipping, clipped signal, speech signal processing, digital signal processing

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Authors: Edward Guillén, Jhordany Rodriguez, Rafael Páez

Abstract:

Anomaly detection techniques have been focused on two main components: data extraction and selection and the second one is the analysis performed over the obtained data. The goal of this paper is to analyze the influence that each of these components has over the system performance by evaluating detection over network scenarios with different setups. The independent variables are as follows: the number of system inputs, the way the inputs are codified and the complexity of the analysis techniques. For the analysis, some approaches of artificial neural networks are implemented with different number of layers. The obtained results show the influence that each of these variables has in the system performance.

Keywords: network intrusion detection, machine learning, artificial neural network, anomaly detection module

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Authors: Antigoni Panagiotopoulou, Lemonia Ragia

Abstract:

High-resolution satellite images can provide detailed information about change detection on the earth. In the present work, QuickBird images of spatial resolution 60 cm/pixel and WorldView images of resolution 30 cm/pixel are utilized to perform automatic change detection in urban and suburban areas of Crete, Greece. There is a relative time difference of 13 years among the satellite images. Multiindex scene representation is applied on the images to classify the scene into buildings, vegetation, water and ground. Then, automatic change detection is made possible by pixel-per-pixel comparison of the classified multi-temporal images. The vegetation index and the water index which have been developed in this study prove effective. Furthermore, the proposed change detection approach not only indicates whether changes have taken place or not but also provides specific information relative to the types of changes. Experimentations with other different scenes in the future could help optimize the proposed spectral indices as well as the entire change detection methodology.

Keywords: change detection, multiindex scene representation, spectral index, QuickBird, WorldView

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Authors: Priyanka Manghani, Manthan Patel, Rahul Peddawad

Abstract:

Cervical Cancer is a major public health hurdle in the area of women’s health. The most common cause of Cervical Cancer is the Human Papilloma Virus (HPV). Human papilloma virus has various genotypes, with HPV 16 and HPV 18 being the major etiological factor causing carcinoma of the Cervix. Early screening and detection by Papanicolaou Smears (PAP) is an effective method for identifying premalignant and malignant lesions. In case of existing pre- malignant lesions /cervical dysplasia’s found with HPV 16 or 18, appropriate follow up can be done to prevent it from developing into a neoplasm. Aims and Objectives: Primary Aim; To study various abnormal cervical cytology reports as detected by Pap Smear Tests, using the Bethesda System in women at a Tertiary Care Hospital. Secondary Aim; To discuss the importance of Pap smear in Cervical Cancer Screening Program. Materials and Methods: Our study is a prospective study, based on 101 women who attended the Out-patient department of Obstetrics and Gynecology at a tertiary care hospital in age group 20-40 years with chief complaints of white/foul vaginal discharge, post-coital Bleeding, low back pain, irregular menstruation, etc. 60 women, who were tested, of the total no of women, were commercial sex workers, thus being a high-risk group for HPV infection. All women underwent conventional cytology. For all the abnormal smears, further cervical biopsies were done, and the final diagnosis was done on the basis of histopathology (gold standard). Results: In all these patients, 16 patients presented with normal smears out of which 2 belonged to the category of commercial sex workers (3.33%) and 14 being from the normal/control group (34.15%). 44 women presented with inflammatory smears out of which 30 were commercial sex workers (50%) and 14 from the control Group (34.15%). A total of 11 women presented with infectious etiology with 6 being commercial sex workers (10%) and 5 (12.2%) being in the control group. A total of 8 patients presented with low-grade squamous intra epithelial lesion (LSIL) with 7 (11.7%) being commercial sex workers and 1(2.44%) patient belonging to the control group. A Total of 7 patients presented with high-grade squamous intraepithelial lesion (HSIL) with 6 (10%) being commercial sex workers and 1 (2.44%) belonging to the control group. 9 patients in total presented with atypical squamous cells of undetermined significance (ASCUS) with 6(10%) being commercial sex workers and 3 (7.32%) belonging to the control group. Squamous cell carcinoma(SCC) presence was found only in 1(1.7%) commercial sex worker. Conclusion – We conclude that HSIL, LSIL, SCC and sexually related infections are comparatively more common in vulnerable groups such as sex workers due to a variety of factors such as multiple sexual partners and poor genital hygiene. Early screening and follow up interventions are highly needed for them along with Health education for risk factors and to emphasize on the importance of Pap smear screening.

Keywords: cervical cancer, papanicolaou (pap) smear, bethesda system, neoplasm

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