Empowering Diagnosis: The Role of CNNs in Prostate Disease Detection

Affecting millions of people globally, prostate disease comprising benign prostatic hyperplasia (BPH), prostatitis, and prostate cancer ranks as the main cause of cancer-related mortality. Although early identification is vital, hand histological examination takes time and is prone to human mistake. This effort intends to automate prostate tissue classification with convolutional neural networks (CNNs), a deep learning method successful in picture recognition. The study uses 1,000 histopathological images of prostate tissues categorized by Gleason score into benign and cancerous grades (g340x, g440x, g540x). A CNN model was trained on this dataset, achieving 90% accuracy in classifying these images. Preprocessing steps, such as image resizing and augmentation, enhanced the model's generalization. The CNN demonstrated strong performance, particularly in distinguishing between benign and advanced cancer grades, with high precision and recall across classes. Four phases-data preparation, model architecture design, training, and evaluation-as recommended define the method. The performance of the model was assessed by means of accuracy, precision, recall, and F1-score, therefore highlighting its resilience in prostate tissue classification. These findings underline how CNN-based systems could reduce pathologists' time, improve diagnostic accuracy, and provide a consistent way of prostate cancer diagnosis. The study reveals that automated methods help early diagnosis and customized therapy plans since they offer a scalable option for prostate disease identification in clinical settings.