I. J. of Electrical & Electronics Research
Research Article | 
Skin Cancer Detection and Segmentation Using Convolutional Neural Network Models
Author(s): Swetha S1, S. Saranya2 and M. Devaraju3
Published In : International Journal of Electrical and Electronics Research (IJEER) Volume 10, Issue 4
Publisher : FOREX Publication
Published : 12 November 2022
e-ISSN : 2347-470X
Page(s) : 984-987
Abstract
Skin cancer is known as one of the killing diseases in humans around the world. In this paper, melanoma skin cancer images are classified and the cancer regions are segmented using Convolutional Neural Networks (CNN). The skin images are data augmented into high number of skin images for obtaining the high classification accuracy. Then, CNN classifier is used to classify the skin image into either melanoma or normal. Finally, morphological segmentation method is used to segment the cancer regions. The simulation results are obtained by applying the proposed methods on ISIC and HAM dataset skin images.
Keywords: Skin, Cancer, melanoma, CNN, segmentation .
Swetha S*, PG Scholar, Communication Systems, Easwari Engineering College, Chennai, India; Email: swethasenthil98@gmail.com
S. Saranya, Assistant Professor, Electronics and communication, Easwari Engineering College, Chennai, India; Email: saranya.s@eec.srmrmp.edu.in
M. Devaraju, Professor, Electronics and communication, Easwari Engineering College, Chennai, India; Email: hod.ece@eec.srmrmp.edu.in
-
[1] R. Dobrescu, M. Dobrescu, S. Mocanu, and D. Popescu, “Medical images classification for skin cancer diagnosis based on combined texture and fractal analysis,” WISEAS Transactions on Biology and Biomedicine, vol. 7, no. 3, pp. 223–232, 2010.[Cross Ref]
-
[2] C. Salem, D. Azar, and S. Tokajian, “An image processing and genetic algorithm-based approach for the detection of melanoma in patients,” Methods of Information in Medicine, vol. 57, no. 1, pp. 74–80, 2018.[Cross Ref]
-
[3] R. Das, S. De, S. Bhattacharyya, J. Platos, V. Snasel, and A. E. Hassanien, “Data augmentation and feature fusion for melanoma detection with content based image classification,” in Proceedings of the International Conference on Advanced Machine Learning Technologies and Applications, Cairo, Egypt, January 2020.[Cross Ref]
-
[4] H. Alquran, I. A. Qasmieh, A. M. Alqudah et al., “The melanoma skin cancer detection and classification using support vector machine,” in Proceedings of the 2017 IEEE Jordan Conference on Applied Electrical Engineering and Computing Technologies (AEECT), IEEE, Aqaba, Jordan, October 2017. [Cross Ref]
-
[5] N. Zhang, Y.-X. Cai, Y.-Y. Wang, Y.-T. Tian, X.-L. Wang, and B. Badami, “Skin cancer diagnosis based on optimized convolutional neural network,” Artificial Intelligence in Medicine, vol. 102, Article ID 101756, 2020.[Cross Ref]
-
[6] Esteva, B. Kuprel, R. A. Novoa et al., “Dermatologist-level classification of skin cancer with deep neural networks,” Nature, vol. 542, no. 7639, pp. 115–118, 2017.[Cross Ref]
-
[7] Shi Wang, Melika Hamian, "Skin Cancer Detection Based on Extreme Learning Machine and a Developed Version of Thermal Exchange Optimization", Computational Intelligence and Neuroscience, vol. 2021, Article ID 9528664, 13 pages, 2021.[Cross Ref]
-
[8] Z. Xu, F. R. Sheykhahmad, N. Ghadimi, and N. Razmjooy, “Computer-aided diagnosis of skin cancer based on soft computing techniques,” Open Medicine, vol. 15, no. 1, pp. 860–871, 2020.[Cross Ref]
-
[9] Pennisi, D. D. Bloisi, D. Nardi, A. R. Giampetruzzi, C. Mondino, and A. Facchiano, “Skin lesion image segmentation using delaunay triangulation for melanoma detection,” Computerized Medical Imaging and Graphics, vol. 52, pp. 89–103, 2016.[Cross Ref]
-
[10] ISIC dataset: https://www.kaggle.com/nodoubttome/skin-cancer9-classesisic[Cross Ref]
-
[11] HAM dataset: https://www.kaggle.com/kmader/skin-cancer-mnist-ham10000[Cross Ref]
Swetha S, S Saranya, M Devaraju (2022), Skin Cancer Detection and Segmentation Using Convolutional Neural Network Models. IJEER 10(4), 984-987. DOI: 10.37391/IJEER.100438.