Leveraging Cutting-Edge Deep Learning Techniques for Superior Prostate Cancer Detection

Prostate cancer is one of the main causes of death due to cancer in men, so diagnostic procedures for this disease must be effective and timely. This paper presents research into the possibility of diagnosing prostate cancer from MRI images of a prostate gland using the YOLOv8 deep learning system. Run on hundreds of images regarding cancer, it showed positive findings with an F1 confidence curve of 0.95, precision confidence of 0.99 with recall confidence of 0.99 and a precision-recall confidence of 0.98. Results show how excellent and effective the algorithm is in detecting prostate cancer. The results further portray that integrating YOLOv8 algorithm can improve diagnosis such that faster and more accurate diagnosis of prostate cancer be made. The study will further be tailored to optimize the algorithm, expanding the dataset to incorporate more prostate cancer variations and introducing new data from MRI. Deployment of the model would demand genomic data, actual time clinical usage of the model, improved interpretability of the model, and compliance with legal retention policy. It opens up possibilities for further research in this field in the future towards betterment in prostate cancer detection and therapy.