Improving Prostate Cancer Diagnosis with Weakly Supervised Learning and Radiology-Confirmed Negative MRI Data

This work demonstrates the effectiveness of convolutional neural networks (CNNs) and proposes a fresh approach to prostate cancer diagnosis through the application of machine learning models. The research makes use of a dataset of 2498 prostate pictures, of which 40% show benign diseases and 60% show cancerous ones. Many machine learning models are trained using comprehensive feature extraction, with an emphasis on texture- and intensity-based variables. With accuracy of 95.8%, precision, recall, and F1 scores of 96.2%, 95.5%, and 95.8%, respectively, CNN performs remarkably well. K-Nearest Neighbors (KNN), Recurrent Neural Network (RNN), and Support Vector Machine (SVM) all show great accuracy with scores of 92.2%, 88.7%, and 86.5%, respectively. A thorough examination can result in more advantages and suitability of various models for prostate cancer screening. CNN is a suitable option for workflow integration in the healthcare industry because of its improved performance metrics and interpretability. With its ability to make better decisions, the accuracy of diagnoses and patient outcomes are improved and also these machine learning models have the potential to fundamentally alter the way prostate cancer is treated. This research shows how computational techniques could revolutionize the way prostate cancer is now identified and open up the opportunity to more accurate and individualized treatment plans.