Skin Lesion Classification Using Convolutional Neural Network and ABCD Rule
Author:
KESTEK Ezgi1ORCID, AKTAN Mehmet Emin2ORCID, AKDOĞAN Erhan1ORCID
Affiliation:
1. YILDIZ TEKNİK ÜNİVERSİTESİ, MAKİNE FAKÜLTESİ, MEKATRONİK MÜHENDİSLİĞİ BÖLÜMÜ, MEKATRONİK MÜHENDİSLİĞİ PR. 2. Bartın Üniversitesi
Abstract
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.
Publisher
Turkish Journal of Mathematics and Computer Science, Association of Mathematicians
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