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 improvingtreatment results. This deadly disease affects men all over the globe and is among the most frequentmalignancies. It used to be that the main ways to identify prostate cancer were by traditional diagnosticprocedures including PSA testing, digital rectal examination (DRE), ultrasound imaging, and histologicalinvestigation;however, these techniques often suffer from limitations such as low specificity, subjectiveinterpretation, operator dependency, and high rates of false positives or false negatives, which may lead todelayed diagnosis or overtreatment. Recent developments in AI have brought deep learning andtraditional 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 likeMRI, CT, and ultrasound scans, deep learning models—especially CNNs and transfer learningarchitectures—have shown great promise for automatically detecting complex patterns, subtle anomalies,and high-dimensional feature representations that might otherwise go undetected by more conventionalmethods. 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. Thesemethods work well alongside deep learning approaches. Improving diagnostic performance is theprimary goal of this project, which is why it's developing and deploying a hybrid framework thatintegrates deep learning with more conventional methods for prostate cancer detection is the primaryemphasis of this study. Initial steps in the approach include normalizing, segmenting, and augmentingmedical imaging data for preprocessing. Then, features are extracted using deep learning models andintegrated with customized statistical and texture-based characteristics. We next assess the performanceof both conventional classifiers and Recall, sensitivity, specificity, accuracy, F1-score, and area under thereceiver operating characteristic (ROC) curve of end-to-end deep learning models employing the featuresthat have been extracted. Reliable early detection is ensured by the system's goal of minimizing falsepositives while retaining high sensitivity. The automated predictions are further enhanced withinterpretability and clinical confidence by including explainability techniques like Grad-CAM and featuresignificance analysis. Incorporating AI-assisted diagnostic systems into real-world healthcare processesis 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 onradiologists and pathologists by facilitating second views, standardizing diagnostic procedures, andlowering effort.Furthermore, the comparative analysis between deep learning and traditional techniquesprovides valuable insights into their respective advantages, limitations, and suitability for different stagesof medical data availability and clinical application. Personalized treatment planning, improved patientsurvival 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.