Skin Cancer Diagnosis Using CNN Features with Genetic Algorithm and Particle Swarm Optimization Methods

Abstract If skin cancer is not treated early, it also affects the diseased area under the skin and this threatens the treatment of the disease. In recent years many diseases have been rapidly detected with high accuracy with artificial intelligence methods and the treatment process has accelerated. Convolutional neural networks one of the artificial intelligence methods provide very detailed information about images and extremely successful results are obtained in classifying images. In this study firstly the data set was trained with the EfficientNetB0 model which is one of the convolutional neural networks models. Then, with the fully connected layer of this model, deep features of the images were obtained. These deep features were obtained by selecting with PSO and GA optimization algorithms, and different feature combinations were created. Each of these selected feature sets were classified by the SVM method and the best performance results were tried to be obtained. As a result, the success of the proposed model has been proven by obtaining an accuracy rate of 89,17%.