An advanced CNN-based method for prostate cancer detection using YOLOv9

Abstract

Prostate cancer (PCa), one of the most common tumours in men, has a high death rate and is occasionally brought on by an inaccurate or delayed diagnosis. The urgent need for new accurate and dependable imaging-based diagnostic tools is highlighted by the low sensitivity and specificity of traditional screening methods such digital rectal examination and prostate-specific antigen (PSA) testing. In this paper, we provide a novel deep learning architecture for prostate cancer diagnosis based on the speed and accuracy of You Only Look Once, version 9 (YOLOv9). Our method minimizes the need for intensive post-processing by precisely localizing and classifying malignant lesions in multiparametric MRI scans via domain-specific data augmentation, adaptive anchor box adjustment, and fine-grained feature fusion. A carefully chosen and annotated collection of prostate pictures was divided into training, validation, and test sets using a patient-wise split to prevent data leakage. On the independent test cohort, the improved YOLOv9 model obtained a precision-recall value of 98%, an F1-score of 96%, a precision-recall of 91%, and a recall of 100%. These findings show a notable improvement in performance over the state-of-the-art techniques currently in use, especially when it comes to reducing false negatives, which is a crucial factor in clinical decision-making. Qualitative heat map analysis further showed that the model consistently focused on clinically important areas that were strongly aligned to expert radiologist observations. This attests to the model's interpretability and appropriateness for clinical real-time applications. In order to improve diagnostic confidence and facilitate early intervention, the suggested YOLOv9-based framework provides a quick, precise, and understandable method for PCa detection. Future studies will focus on merging multimodal imaging data and validating the model across bigger, multi-center surveys to ensure generalisability and therapeutic use.