Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 32727
Automatic Classification of Lung Diseases from CT Images

Authors: Abobaker Mohammed Qasem Farhan, Shangming Yang, Mohammed Al-Nehari


Pneumonia is a kind of lung disease that creates congestion in the chest. Such pneumonic conditions lead to loss of life due to the severity of high congestion. Pneumonic lung disease is caused by viral pneumonia, bacterial pneumonia, or COVID-19 induced pneumonia. The early prediction and classification of such lung diseases help reduce the mortality rate. We propose the automatic Computer-Aided Diagnosis (CAD) system in this paper using the deep learning approach. The proposed CAD system takes input from raw computerized tomography (CT) scans of the patient's chest and automatically predicts disease classification. We designed the Hybrid Deep Learning Algorithm (HDLA) to improve accuracy and reduce processing requirements. The raw CT scans are pre-processed first to enhance their quality for further analysis. We then applied a hybrid model that consists of automatic feature extraction and classification. We propose the robust 2D Convolutional Neural Network (CNN) model to extract the automatic features from the pre-processed CT image. This CNN model assures feature learning with extremely effective 1D feature extraction for each input CT image. The outcome of the 2D CNN model is then normalized using the Min-Max technique. The second step of the proposed hybrid model is related to training and classification using different classifiers. The simulation outcomes using the publicly available dataset prove the robustness and efficiency of the proposed model compared to state-of-art algorithms.

Keywords: CT scans, COVID-19, deep learning, image processing, pneumonia, lung disease.

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 437


[1] Elibol, E. Otolaryngological symptoms in COVID-19. Eur Arch Otorhinolaryngol (2020).
[2] Padda, I., Khehra, N., Jaferi, U. et al. The Neurological Complexities and Prognosis of COVID-19. SN Compr. Clin. Med. 2, 2025–2036 (2020).
[3] Pagliano, P., Sellitto, C., Conti, V. et al. Characteristics of viral pneumonia in the COVID-19 era: an update. Infection 49, 607–616 (2021).
[4] Sun, W., Zheng, B., & Qian, W. (2016). Computer aided lung cancer diagnosis with deep learning algorithms. Medical Imaging 2016: Computer-Aided Diagnosis. doi:10.1117/12.2216307.
[5] Peng, S., Chen, J., Zhang, W., Zhang, B., Liu, Z., Liu, L., … Lv, F. (2021). The role of chest CT quantitative pulmonary inflammatory index in the evaluation of the course and treatment outcome of COVID-19 pneumonia. Scientific Reports, 11(1). doi:10.1038/s41598-021-87430-5.
[6] Shirani, F., Shayganfar, A. & Hajiahmadi, S. COVID-19 pneumonia: a pictorial review of CT findings and differential diagnosis. Egypt J Radiol Nucl Med 52, 38 (2021).
[7] Mahajan, H.B., Badarla, A. & Junnarkar, A.A. CL-IoT: cross-layer Internet of Things protocol for intelligent manufacturing of smart farming. J Ambient Intell Human Comput 12, 7777–7791 (2021).
[8] Mahajan, H.B., Badarla, A. Cross-Layer Protocol for WSN-Assisted IoT Smart Farming Applications Using Nature Inspired Algorithm. Wireless Pers Commun (2021).
[9] Uke, N., Pise, P., Mahajan, H.B., (2021). Healthcare 4.0 Enabled Lightweight Security Provisions for Medical Data Processing. Turkish Journal of Computer and Mathematics (2021), Vol. 12, No. 11.
[10] Zhang, K., Liu, X., Shen, J., Li, Z., Sang, Y., Wu, X., … Wang, G. (2020). Clinically Applicable AI System for Accurate Diagnosis, Quantitative Measurements, and Prognosis of COVID-19 Pneumonia Using Computed Tomography. Cell, 182(5), 1360. doi:10.1016/j.cell.2020.08.029.
[11] Zhao, W., Jiang, W. & Qiu, X. Deep learning for COVID-19 detection based on CT images. Sci Rep 11, 14353 (2021).
[12] Ning, W., Lei, S., Yang, J., Cao, Y., Jiang, P., Yang, Q., … Wang, Z. (2020). Open resource of clinical data from patients with pneumonia for the prediction of COVID-19 outcomes via deep learning. Nature Biomedical Engineering, 4(12), 1197–1207. doi:10.1038/s41551-020-00633-5
[13] Gunraj, H., Wang, L., & Wong, A. (2020). COVIDNet-CT: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest CT Images. Frontiers in Medicine, 7. doi:10.3389/fmed.2020.608525
[14] Wang, S., Kang, B., Ma, J., Zeng, X., Xiao, M., Guo, J., … Xu, B. (2021). A deep learning algorithm using CT images to screen for Corona virus disease (COVID-19). European Radiology. doi:10.1007/s00330-021-07715-1
[15] Yang, Y., Lure, F. Y. M., Miao, H., Zhang, Z., Jaeger, S., Liu, J., & Guo, L. (2020). Using artificial intelligence to assist radiologists in distinguishing COVID-19 from other pulmonary infections. Journal of X-Ray Science and Technology, 1–17. doi:10.3233/xst-200735
[16] Bai, H. X., Wang, R., Xiong, Z., Hsieh, B., Chang, K., Halsey, K., … Liao, W.-H. (2020). Artificial Intelligence Augmentation of Radiologist Performance in Distinguishing COVID-19 from Pneumonia of Other Origin at Chest CT. Radiology, 296(3), E156–E165. doi:10.1148/radiol.2020201491
[17] Fan, D.-P., Zhou, T., Ji, G.-P., Zhou, Y., Chen, G., Fu, H., … Shao, L. (2020). Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Images. IEEE Transactions on Medical Imaging, 39(8), 2626–2637. doi:10.1109/tmi.2020.2996645
[18] Wang, X., Deng, X., Fu, Q., Zhou, Q., Feng, J., Ma, H., … Zheng, C. (2020). A Weakly-supervised Framework for COVID-19 Classification and Lesion Localization from Chest CT. IEEE Transactions on Medical Imaging, 1–1. doi:10.1109/tmi.2020.2995965
[19] Tan, W., Liu, P., Li, X., Liu, Y., Zhou, Q., Chen, C., … Zhang, Y. (2021). Classification of COVID-19 pneumonia from chest CT images based on reconstructed super-resolution images and VGG neural network. Health Information Science and Systems, 9(1). doi:10.1007/s13755-021-00140-0
[20] Shiri, I., Arabi, H., Salimi, Y., (2021). COLI-Net: Deep learning-assisted fully automated COVID-19 lung and infection pneumonia lesion detection and segmentation from chest computed tomography images. Int J Imaging Syst Technol. 2021;1–14. 10.1002/ima.22672.
[22] Mahajan, H.B., Uke, N., Pise, P. et al. Automatic robot Manoeuvres detection using computer vision and deep learning techniques: a perspective of internet of robotics things (IoRT). Multimed Tools Appl (2022).