PROSTATE CANCER DETECTION USING DEEP LEARNING AND TRADITIONAL TECHNIQUES

Abstract

When it comes to prostate cancer, early identification is key to lowering death rates and improving treatment results. This deadly disease affects men all over the globe and is among the most frequent malignancies. It used to be that the main ways to identify prostate cancer were by traditional diagnostic procedures including PSA testing, digital rectal examination (DRE), ultrasound imaging, and histological investigation;however, these techniques often suffer from limitations such as low specificity, subjective interpretation, operator dependency, and high rates of false positives or false negatives, which may lead to delayed diagnosis or overtreatment. Recent developments in AI have brought deep learning and traditional machine learning approaches as potent resources to aid physicians in decision-making, automate feature extraction, and increase diagnostic accuracy. When applied to medical imaging data like MRI, CT, and ultrasound scans, deep learning models—especially CNNs and transfer learning architectures—have shown great promise for automatically detecting complex patterns, subtle anomalies, and high-dimensional feature representations that might otherwise go undetected by more conventional methods. When it comes to situations where there are limited datasets, well-defined handcrafted features, and lower computational costs, traditional machine learning methods like k-nearest neighbors (KNN), decision trees, support vector machines (SVM), and random forests still have their advantages. These methods work well alongside deep learning approaches. Improving diagnostic performance is the primary goal of this project, which is why it's developing and deploying a hybrid framework that integrates deep learning with more conventional methods for prostate cancer detection is the primary emphasis of this study. Initial steps in the approach include normalizing, segmenting, and augmenting medical imaging data for preprocessing. Then, features are extracted using deep learning models and integrated with customized statistical and texture-based characteristics. We next assess the performance of both conventional classifiers and Recall, sensitivity, specificity, accuracy, F1-score, and area under the receiver operating characteristic (ROC) curve of end-to-end deep learning models employing the features that have been extracted. Reliable early detection is ensured by the system's goal of minimizing false positives while retaining high sensitivity. The automated predictions are further enhanced with interpretability and clinical confidence by including explainability techniques like Grad-CAM and feature significance analysis. Incorporating AI-assisted diagnostic systems into real-world healthcare processes is a viable possibility, and this research shows that hybrid models can withstand real-world challenges. Particularly in areas lacking access to qualified experts, the suggested paradigm may ease the burden on radiologists and pathologists by facilitating second views, standardizing diagnostic procedures, and lowering effort.Furthermore, the comparative analysis between deep learning and traditional techniques provides valuable insights into their respective advantages, limitations, and suitability for different stages of medical data availability and clinical application. Personalized treatment planning, improved patient survival rates, and a contribution to precision medicine are some of the long-term goals of the study, which ultimately highlights the importance of AI in transforming prostate cancer diagnosis.