Classification of Skin Lesion Images with Deep Learning Approaches - Bayram, Buket; Kulavuz, Bahadir; Ertugrul, Berkay; Bayram, Bulent; Bakirman, Tolga; Cakar, Tuna; Dogan, Metehan (UNIV LATVIA, 2022)
Skin cancer is one of the most dangerous cancer types in the world. Like any other cancer type, early detection is the key factor for the patient???s recovery. Integration of artificial intelligence with medical image processing can aid to decrease misdiagnosis. The purpose of the article is to show that deep learning-based image classification can aid doctors in the healthcare field for better diagnosis of skin lesions. VGG16 and ResNet50 architectures were chosen to examine the effect of CNN networks on the classification of skin cancer types. For the implementation of these networks, the ISIC 2019 Challenge has been chosen due to the richness of data. As a result of the experiments, confusion matrices were obtained and it was observed that ResNet50 architecture achieved 91.23% accuracy and VGG16 architecture 83.89% accuracy. The study shows that deep learning methods can be sufficiently exploited for skin lesion image classification.
Keywords: Deep Learning; Image classification; ISIC 2019; ResNet50; VGG16; Computer Science