Ensemble Model for Prostate Cancer Detection Using MRI Images

Prostate cancer is a prevalent form of malignancy impacting a substantial male population and ranks among the primary contributors to cancer-related fatalities globally. The utilization of magnetic resonance imaging (MRI) scans for prostate cancer detection has presented significant difficulties. This proposed, explores the use of machine learning algorithms for prostate cancer detection using MRI scans and addresses the challenge mentioned above. The proposed ensemble models employ a combination of Machine Learning (ML) Algorithms, including Support Vector Machine (SVM), AdaBoost, Decision Tree (DT), and Random Forest (RF) to improve accuracy detection. The results of our rigorous evaluation process revealed that the ensemble model achieved an outstanding accuracy rate of 96% in classifying prostate cancer into Significant and Non-Significant. By comparing our results to existing studies, we have demonstrated that the ensemble model-based method is on par with or even surpasses various techniques used in previous research efforts.