Skin Lesion Image Segmentation Using Machine Learning

Abstract

Melanoma is a sort of skin disease that represents more than seventy-five percent of all skin diseases connected to all fatalities. Nonetheless, doctors have demonstrated that the probability rate of patients improves radically with early analysis and diagnosis. This motivated researchers to seek automated techniques that facilitate early diagnosis of skin cancer. Skin lesion segmentation is a significant advance in the analysis and the resulting treatment of melanoma. Automatic lesion segmentation is of major interest for early detection and treatment of skin cancer, because it provides better accuracy and speed, compared to manual analysis. Lately, deep neural networks have provided better results for medical image segmentation, compared to classical approaches based on machine learning. In this thesis, first an extensive a review of existing deep network architectures that have been suggested to segment skin lesions, pre-processing and post-processing methods with the available datasets that can be used for research in this area, also presented a comparison between the results of different methods used for skin lesion segmentation showing the strengths and weaknesses of each method.