DIAGNOSIS OF BREAST CANCER USING MULTISCALE CONVOLUTIONAL NEURAL NETWORK

Abstract

The diagnosis of benign and malignant breast cancer is a challenging issue today. Breast cancer is the most common cancer that women suffer from. The sooner the cancer is detected, the easier and more successful it is to treat it. The most common diagnostic method is the mammography of a simple radiographic picture of the chest. The use of image processing techniques and identifying patterns in the detection of breast cancer from mammographic images reduces human errors in detecting tumors, and speeds up diagnosis time. Artificial Neural Network (ANN) has been widely used to detect breast cancer, and has significantly reduced the percentage of errors. Therefore, in this paper, Convolution as Neural Network (CNN), which is the most effective method, is used for the detection of various types of cancers. This study presents a Multiscale Convolutional Neural Network (MCNN) approach for the classification of tumors. Based on the structure of MCNN, which presents mammography picture to several deep CNN with different size and resolutions, the classical handcrafted features extraction step is avoided. The proposed approach gives better classification rates than the classical state-of-the-art methods allowing a safer Computer-Aided Diagnosis of pleural cancer. This study reaches the diagnosis accuracy of [Formula: see text] using multiscale convolution technique which reveals the efficient proposed method.