Segmentation And Automatic Classification Of Skin Lesion Using Neural Networks

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
© 2021 by IJETT Journal
Volume-69 Issue-1
Year of Publication : 2021
Authors : Er. Simrandeep Singh
DOI :  10.14445/22315381/IJETT-V69I1P218


MLA Style: Er. Simrandeep Singh. "Segmentation And Automatic Classification Of Skin Lesion Using Neural Networks" International Journal of Engineering Trends and Technology 69.1(2021):116-120. 

APA Style:Er. Simrandeep Singh. Segmentation And Automatic Classification Of Skin Lesion Using Neural Networks  International Journal of Engineering Trends and Technology, 69(1), 116-120.

Melanoma is a lethal disease often impossible to cure if detected at later stages. To save a person, it is necessary to detect melanoma at earlier stages because melanoma treatment is detected at prior stages. Due to several reasons, there is a need for an automated system to detect melanoma. An automated system for the segmentation and the skin lesion classification is proposed using the artificial neural network. Image segmentation is carried out to bifurcate input image into different clusters. In this proposed methodology, the Fuzzy C Means algorithm is used for the pre-segmentation, and then Gaussian Mixture Model is used for the modeling. The results of the Gaussian mixture model are not as efficient up to the desired level. Artificial Neural Networks are used to achieve the highest accuracy, and their accuracy is checked by using the parameters of sensitivity and specificity.

[1] G. S. Jayalakshmi and V. S. Kumar., Performance analysis of convolutional neural network (CNN) based cancerous skin lesion detection system, in ICCIDS 2019 - 2nd International Conference on Computational Intelligence in Data Science, Proceedings, (2019).
[2] A. Rajesh., Classification of malignant melanoma and Benign Skin Lesion using backpropagation neural network and ABCD rule, in Proceedings - 2017 IEEE International Conference on Electrical, Instrumentation and Communication Engineering, ICEICE 2017, 2017,(2017) 1–8.
[3] M. Grochowski, A. Miko?ajczyk, and A. Kwasigroch, Diagnosis of malignant melanoma by neural network ensemble-based system utilizing hand-crafted skin lesion features, Metrol. Meas. Syst., 26, (1)(2019) 65–80.
[4] B. Goyal, A. Dogra, S. Agrawal, B. S. Sohi, and A. Sharma., Image denoising review: From classical to state-of-the-art approaches, Inf. FUSION, 55(2020) 220–244.
[5] M. Kaur and V. Wasson, "ROI Based Medical Image Compression for Telemedicine Application," in Procedia Computer Science, 70(2015) 579–585.
[6] A. Gupta, D. Singh, and M. Kaur, An efficient image encryption using non-dominated sorting genetic algorithm-III based 4-D chaotic maps Image encryption, J. Ambient Intell. Humaniz. Comput., 11(3) (2020), SI, 1309–1324.
[7] A. Mahbod, G. Schaefer, C. Wang, R. Ecker, and I. Ellinge, Skin Lesion Classification Using Hybrid Deep Neural Networks," in ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings,(2019) 1229–1233.
[8] I. González-Díaz, DermaKNet: Incorporating the Knowledge of Dermatologists to Convolutional Neural Networks for Skin Lesion Diagnosis, IEEE J. Biomed. Heal. Informatics, 23(2)(2019) 547–559.
[9] L. Bi, D. D. Feng, M. Fulham, and J. Kim., Multi-Label classification of multi-modality skin lesion via a hyper-connected convolutional neural network., Pattern Recognit.,107(2020).
[10] A. Saha, P. Prasad, and A. Thabit., Leveraging Adaptive Color Augmentation in Convolutional Neural Networks for Deep Skin Lesion Segmentation, in Proceedings - International Symposium on Biomedical Imaging, 2020-2014–2017.
[11] W. Sae-Lim, W. Wettayaprasit, and P. Aiyarak.,Convolutional Neural Networks Using MobileNet for Skin Lesion Classification, in JCSSE 2019 - 16th International Joint Conference on Computer Science and Software Engineering: Knowledge Evolution Towards Singularity of Man-Machine Intelligence, (2019) 242–247.
[12] S. Nasiri, J. Helsper, M. Jung, and M. Fathi, DePicT Melanoma Deep-CLASS: A deep convolutional neural networks approach to classify skin lesion images, BMC Bioinformatics, 21(2020).

Skin Cancer, Melanoma, Gaussian Mixture Model, Artificial Neural Network, Innovation.