Advanced Machine Learning Based Prostrate Cancer Detection
Dipankar Sharma, Rahul Gupta, R. N. Ashlin Deepa, G. Niranjana, Rajendrane Rajmohan · 2025
Prostate cancer is a major public health problem, and early diagnosis is paramount in improving therapy outcomes. The traditional interpretation of MRI is made difficult by the heterogeneity of the gland in tissue composition, which may lead to potential diagnostic variability. This work proposes a machine learning-based framework that employs deep learning methods for accurate and early detection of prostate cancer. The system integrates ResNet with transfer learning to enhance feature extraction and thus complements an existing SVM classifier in making final decisions. Pre-processing steps enhance image quality, followed by automatic segmentation of regions of interest using advanced segmentation. The integration of deep feature extraction and machine learning classification improves diagnostic accuracy with minimal human intervention. The novelty is the integration of transfer learning with conventional classifiers, which enables improved interpretability and efficiency. By providing a holistic and automated diagnostic system, the system helps clinicians make timely and accurate clinical decisions, which ultimately lead to better patient outcomes.