Classification of prostate cancer using Deep Learning approach and MobileNetV2 architecture

Since prostate cancer is one of the most important causes of mortality in today's society, the study of why and how to diagnose and predict them has received much attention from researchers. The collaboration of computer and medical experts offers a new solution in analyzing this data and obtaining useful and practical models, which is data mining. In fact, data mining, as one of the most important tools for data analysis and discovering the relationships between them and predicting the occurrence of events is one of the practical tools of researchers in this way. This study diagnoses and classifies prostate cancer using Deep Learning approach and MobileNetV2 architecture based on a method to identify the factors affecting this disease. In this study, data was taken from a database on the Brigham Hospital website. Also, in order to improve the methods of diagnosing prostate cancer, a feature-classification approach has been proposed, which has been evaluated using a data set related to clients' files. The proposed method after applying various classification methods on the available data including benign and malignant diagnosis and reaching an optimal method with relatively high accuracy using a faster R-CNN network to segment the area and later using architecture Various convolutional neural networks (CNNs) have been selected for feature extraction and set classification, increased processing speed. In addition, the MobileNetV2 architecture is used, which has the ability to achieve AUC in the range of 0.87 to 0.95 with acceptable performance, high processing speed and relative accuracy for the diagnosis of prostate cancer.