What question did this study set out to answer?

This study aims to classify skin lesions into normal, abnormal, and melanoma using machine learning techniques.

December 20, 2017Open Access

Skin Lesion Classification using Machine Learning Algorithms

Key Points

This study aims to classify skin lesions into normal, abnormal, and melanoma using machine learning techniques.
Used dermoscopic images from the PH2 dataset for classification.
Employed four machine learning algorithms: ANN, SVM, KNN, and Decision Tree.
Calculated correct classification rates for each method.
Correct classification rates were 92.50% for ANN, 89.50% for SVM, 82.00% for KNN, and 90.00% for Decision Tree.
The developed system serves as a decision support tool for dermatologists.
Indicates potential to improve the accuracy of skin lesion diagnosis.

Abstract

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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