Skin Lesion Classification Using Convolutional Neural Network and ABCD Rule

Skin cancer, which can occur in any part of the human skin, is one of the common and serious types of cancer. Accurate diagnosis and segmentation of lesions are crutial to the early diagnosis. Computer-aided diagnosis make important contributions to help doctors in the diagnosis of cancer from skin images. The most important factor for such systems to reveal the accurate results is the correct feature extraction. In this study, a model for the classification of seven types of skin lesions was developed by combining the features of CNN-based feature extraction and the ABCD rule, which is widely used in the clinic. The model was evaluated on HAM10000 well-known dataset. The classification results obtained with different combinations of features and machine learning algorithms were compared. According to the results, the best classification accuracy was obtained with the Cosine Similarity Classifier with 96.4% when the features determined by CNN and the features in the ABCD rule were used together.