Study and meta-analysis on the degree of malignancy in prostate and breast cancer and its accurate diagnosis using deep neural network

Cells are the basic units that make up the human body. Cells grow and divide to make new cells as the body needs them. Usually, cells die when they get too old or damaged. Then, new cells take their place. Cancer begins when genetic changes interfere with this orderly process. Cells start to grow uncontrollably. These cells may form a mass called a tumor. A tumor can be cancerous or benign. A cancerous tumor is malignant, meaning it can grow and spread to other parts of the body. A benign tumor means the tumor can grow but will not spread. Possible signs and symptoms include a lump, abnormal bleeding, prolonged cough, unexplained weight loss, and a change in bowel movements. While these symptoms may indicate cancer, they can also have other causes. Over ۱۰۰ types of cancers affect humans. In recent years, interest in research into the application of intelligent algorithms for diagnosis and categorization of diseases, especially cancer has increased dramatically. Tumor classification is an important task in medical diagnosis. Technological calculations are important due to their classification function in diagnosis of medical illnesses. Diagnosing and classifying medical images is a challenging task. To detect the malignancy of prostate cancer and the opioid or malignant breast cancer, deep neural network classifier, which is based on Tensor flow framework and Keras library, is used. In the training phase, educational images are considered along with the output class for the network. During training, the weight of the filter is updated every time. However, after several replications, optimal weights are updated and the network is trained to extract the best feature from the images. In this research, the proposed method due to using deep neural network and accurate feature extraction provides detection accuracy about ۹۵.۸۳% and ۹۹.۵% for breast and prostate cancers, respectively, which is more than ۷% compared to other methods. Cancer is one of the most prevalent diseases in the world. Cancer is started from the cells, which are the basic building blocks making the tissue. One of the challenges in medical diagnostic techniques is the difficulty in analyzing dense tissues.

Keywords:

Deep learning , Prostate cancer , Breast cancer , Feature extraction.