A review of neural network detection methods for breast cancer: review article

Breast cancer is the most common cancer among women and the earlier it is diagnosed, the easier it is to treat. The most common way to diagnose breast cancer is mammography. Mammography is a simple chest x-ray and a tool for early detection of non-palpable breast cancers and tumors. However, due to some limitations of this method such as low sensitivity especially in dense breasts, other methods such as 3d mammography, ultrasound and magnetic resonance imaging are often suggested to obtain additional useful information. Recently, computer-aided diagnostic or intelligent diagnostic have been developed to assist radiologists to improve diagnostic accuracy. In general, a computer system consists of four steps: pre-processing, dividing areas of interest, extracting and selecting features, and finally classification. Nowadays, the use of imaging techniques in the identification of patterns for diagnosis and automatic determination of breast cancer by mammography and even digital pathology (which is one of the emerging trends in modern medicine) reduces human errors and speeds up the diagnosis. In this article, We reviewed recent findings and their disadvantages and benefits in the diagnosis of breast cancer by neural networks, especially the artificial neural network, which is widely used in the diagnosis of cancers and intelligent breast cancers. This literature review shows that hybrid algorithms have been better at improving classification and detection accuracy. Providing a convenient way to diagnose tumors in the breast by computer-assisted diagnosis systems will be of great help to the physicians. Much work has been done in recent years to diagnose breast cancer, and many advances have been made in improving and diagnosing breast cancer by computer. All methods have a significant error percentage and are different depending on the type of breast, but compared to other types of neural networks, convolution and combining methods with convo have better results. Another advantage of the convoluted network is the automatic extraction of desirable features. Today, the best percentages of accuracy in detecting benign or malignant cancerous mass are achieved by convolution.