Commenced in January 2007
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Edition: International
Paper Count: 31181
Computer Countenanced Diagnosis of Skin Nodule Detection and Histogram Augmentation: Extracting System for Skin Cancer

Authors: S. Zith Dey Babu, S. Kour, S. Verma, C. Verma, V. Pathania, A. Agrawal, V. Chaudhary, A. Manoj Puthur, R. Goyal, A. Pal, T. Danti Dey, A. Kumar, K. Wadhwa, O. Ved

Abstract:

Background: Skin cancer is now is the buzzing button in the field of medical science. The cyst's pandemic is drastically calibrating the body and well-being of the global village. Methods: The extracted image of the skin tumor cannot be used in one way for diagnosis. The stored image contains anarchies like the center. This approach will locate the forepart of an extracted appearance of skin. Partitioning image models has been presented to sort out the disturbance in the picture. Results: After completing partitioning, feature extraction has been formed by using genetic algorithm and finally, classification can be performed between the trained and test data to evaluate a large scale of an image that helps the doctors for the right prediction. To bring the improvisation of the existing system, we have set our objectives with an analysis. The efficiency of the natural selection process and the enriching histogram is essential in that respect. To reduce the false-positive rate or output, GA is performed with its accuracy. Conclusions: The objective of this task is to bring improvisation of effectiveness. GA is accomplishing its task with perfection to bring down the invalid-positive rate or outcome. The paper's mergeable portion conflicts with the composition of deep learning and medical image processing, which provides superior accuracy. Proportional types of handling create the reusability without any errors.

Keywords: Morphology, Detection, Image Segmentation, computer-aided system

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References:


[1] Aarts, Johanna W. M., Theodoor E. Nieboer, Neil Johnson, Emma Tavender, Ray Garry, Ben Willem J. Mol, and Kirsten B. Kluivers. 2015. "Surgical Approach to Hysterectomy for Benign Gynaecological Disease." Cochrane Database of Systematic Reviews.
[2] Craythorne, Emma, and Firas Al-Niami. 2017. "Skin Cancer." Medicine (United Kingdom).
[3] Davidovic, Monika, Louise Karjalainen, Göran Starck, Elisabet Wentz, Malin Björnsdotter, and Håkan Olausson. 2018. “Abnormal Brain Processing of Gentle Touch in Anorexia Nervosa.” Psychiatry Research - Neuroimaging.
[4] Harish Reddy, K., and T. J. Nagalakshmi. 2019. "Skin Cancer Detection Using Image Processing Technique." International Journal of Engineering and Advanced Technology.
[5] Hay, Roderick J., Nicole E. Johns, Hywel C. Williams, Ian W. Bolliger, Robert P. Dellavalle, David J. Margolis, Robin Marks, Luigi Naldi, Martin A. Weinstock, Sarah K. Wulf, Catherine Michaud, Christopher J.l. Murray, and Mohsen Naghavi. 2014. "The Global Burden of Skin Disease in 2010: An Analysis of the Prevalence and Impact of Skin Conditions." Journal of Investigative Dermatology.
[6] Lundervold, Alexander Selvikvåg, and Arvid Lundervold. 2019. “An Overview of Deep Learning in Medical Imaging Focusing on MRI.” Zeitschrift Fur Medizinische Physik.
[7] Maglogiannis, Ilias, and Charalampos N. Doukas. 2009. "Overview of Advanced Computer Vision Systems for Skin Lesions Characterization." IEEE Transactions on Information Technology in Biomedicine.
[8] Masood, Ammara, and Adel Ali Al-Jumaily. 2013. "Computer Aided Diagnostic Support System for Skin Cancer: A Review of Techniques and Algorithms." International Journal of Biomedical Imaging.
[9] National Cancer Institute. 2018. "Melanoma of the Skin - Cancer Stat Facts.” National Cancer Institute.