Prediction of Prostate Cancer Using MRI Image Data with Convolutional Neural Network Algorithm

This study aims to investigate the effectiveness of the application of deep learning algorithms in predicting prostate cancer through the analysis of magnetic resonance image (MRI) data. Prostate cancer is one disease that is often difficult to predict in the early stages, so a more accurate and efficient prediction method is needed. In this study, we used deep learning techniques, specifically Convolutional Neural Network (CNN), to classify MRI images of the prostate into two categories: normal and prostate cancer. The data used in this study was a dataset of prostate MRI images that had been manually annotated by radiologists. The process of training deep learning models involves the augmentation of data to improve model performance and reduce the risk of overfitting. In addition, we evaluate model performance using metrics such as accuracy, precision, recall, and F1-score. The results of this study are expected to contribute to the development of decision support systems that can assist doctors in diagnosing prostate cancer early. The application of deep learning algorithms to MRI image data is expected to increase the accuracy and speed of the disease prediction process, thus enabling more effective and timely treatment. This research is the first step in the development of prostate cancer prediction technology that can be implemented in daily medical practice.