DEEP LEARNING APPROACH FOR CLASSIFICATION OF BREAST CANCER

Abstract

Breast cancer is one of the most common cancer types diagnosed in the ladies worldwide. Statistics indicate that breast cancer rate is about 12 % in all cancer cases in the world. Also, approximately 25% of women have breast cancer. Therefore, rapid and accurate analysis of medical images obtained with breast cancer is extremely important for diagnosis. Many methods have been used to classify breast cancer. One of the most important methods among these methods is deep learning-based methods. The most important reason for choosing the deep learning model for breast cancer diagnosis is that it can give faster and more accurate results than the existing methods. Because early diagnosis is always important. In this study, a total of 700 images, including benign and malign variants of breast cancer images, were analyzed using the convolutional neural network method. The data set used in this study is publicly accessible. As a result, the histopathologic images of the breast cancer features were extracted using the AlexNet method which is one of the deep learning approaches. The classification process was performed with Support Vector Machines and accuracy of 93.4% was achieved.