Derin öğrenme kullanilarak cilt kanserinin tespiti ve siniflandirilmasi

One of the worst malignancies is skin cancer. It is expected to spread to other parts of the body if it is not identified and treated at the outset. The method for successfully treating skin cancer uses both image processing and deep learning. In this research, three different kinds of skin cancer, including melanoma, pigmented benign keratoses, and basal cell carcinoma are introduced for detection and classification using efficient methods in Machine Learning (ML) such as K-Mean clustering and Multi-class support vector machine (M-SVM) algorithm, and then Deep Learning (DL) techniques such as AlexNet and ResNet are used. Additionally, deep convolutional neural networks (CNN) effectiveness and capacity are seen. The data set contains 1176 images of skin cancer with different classes of disease, this data set is used in both ML and DL. In ML the data set is divided into training and testing sets, these sets are pass through a few steps of enhancement using a canny edge detection filter and extracting the features using the Gray-Level Co-occurrence Matrix (GLCM) method. Then, these images are segmented into three clustering using K-mean clustering algorithms. In DL two models are used AlexNet and ResNet, in these models the data sets are divided into training and testing sets. Then, an augmentation technique has been proposed, it's very useful for small data sets, and the results show changes in accuracy result. The results show an accuracy of 99.03%, and 97.02% for AlexNet, and ResNet respectively.