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Authors
Affiliations
1 Assistant Professor, Department of ECE, Kongu Engineering College, Perundurai, IN
2 Professor, Department of ECE, Kongu Engineering College, Perundurai, IN
3 Student, Department of ECE, Kongu Engineering College, Perundurai, IN
Source
Indian Journal of Public Health Research & Development, Vol 11, No 2 (2020), Pagination: 652-657
Abstract
In today’s modern world, diseases are increasing day by day. Amid the different types of diseases, cancer has turned out to be a deadliest disease in human beings. According to the latest medical statistics, Cancer is accountable for high mortality rate. Skin cancer is usually identified beginning with preliminary screening by clinicians and followed possibly by dermoscopic investigation, a biopsy and histopathological inspection. Differentiating and predicting different types of skin lesions on inspection of images is one of the most challenging and difficult task because of the minute variations in the appearance of skin lesions. The images of different lesions seem to be identical and it is very difficult to manually differentiate them. Hence, deep convolutional neural networks are now emerging as a solution to address this problem. The main objective of this work is to review the indexed papers that addresses the issues of automatic classification of skin lesions. This paper summarizes about the different types of datasets available, the type of deep learning models used for training and the parameters used for performance measurements.
Keywords
Skin lesion classification, Deep Learning, Data Augmentation, Artifacts, Transfer Learning.
