Skin Lesion Classification using Machine Learning Algorithms
Melanoma is a deadly skin cancer that breaks out in the skin’s pigment cells on the skin surface. Melanoma causes 75% of the skin cancer-related deaths. This disease can be diagnosed by a dermatology specialist through the interpretation of the dermoscopy images in accordance with ABCD rule. Even if dermatology experts use dermatological images for diagnosis, the rate of the correct diagnosis of experts is estimated to be 75-84%. The purpose of this study is to pre-classify the skin lesions in three groups as normal, abnormal and melanoma by machine learning methods and to develop a decision support system that should make the decision easier for a doctor. The objective of this study is skin lesions based on dermoscopic images PH2 datasets using 4 different machine learning methods namely; ANN, SVM, KNN and Decision Tree. Correctly classified instances were found as 92.50%, 89.50%, 82.00% and 90.00% for ANN, SVM, KNN and DT respectively. The findings show that the system developed in this study has the feature of a medical decision support system which can help dermatologists in diagnosing of the skin lesions.
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