Detection of Malignancy Degree in Prostate and Breast Cancers by Using Deep Neural Network
Saber Fooladi, Hassan Farsi, Sajad Mohamadzadeh · 2019
Abstract Introduction: 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. Materials and Methods: 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. Results: In this research, the proposed method due to using deep neural network and accurate feature extraction provides detection accuracy about 95.83 and 99.5 for breast and prostate cancers, respectively, which is more than 7 compared to other methods. Conclusion: 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. Since the detection of the diagnosis by human is time-consuming and has a higher probability of error, the researchers have been trying to detect it automatically by using different algorithms.