Revolutionizing Prostate Cancer Diagnosis: Harnessing the Potential of Transfer Learning for MRI-Based Classification

Abstract

This study introduces a novel framework that harnesses the potential of transfer learning for MRI -based classification in prostate cancer diagnosis. The study explores the challenges of accurately classifying prostate cancer from MRI images and proposes a methodology that integrates pre-trained deep neural networks with a large, annotated dataset. The framework demonstrates superior performance compared to conventional methods, achieving higher Accuracy, Precision, Recall, Fl-Score. The approach exhibits robustness across different MRI scanners and patient populations, highlighting its potential for real-world clinical applications. Additionally, this study emphasizes the interpretability of the model through visualization techniques, aiding clinicians in understanding the underlying pathology and making informed treatment decisions. Overall, this research contributes to the revolutionization of prostate cancer diagnosis, offering improved accuracy and efficiency in clinical settings and paving the way for advanced computer-aided diagnostic systems.