Automated prostate cancer grading and diagnosis system using deep learning-based Yolo object detection algorithm

Abstract

Purpose: Developing an artificial intelligence-based prostate cancer detection and diagnosis system that can automatically determine important regions and accurately classify the determined regions on an input biopsy image. Method: The Yolo general-purpose object detection algorithm was utilized to detect important regions (for the localization task) and to grade the detected regions (for the classification task). The algorithm was re-trained with our prostate cancer dataset. The dataset was created by annotating 500 real prostate tissue biopsy images. The dataset was split into train/test parts as 450/50 real prostate tissue images, respectively, before the data augmentation process. Next, the training set consisting of 450 labeled biopsy images was pre-processed with the data augmentation method. This way, the number of biopsy images in the dataset was increased from 450 to 1776. Then, the algorithm was trained with the dataset and the automatic prostate cancer detection and diagnosis tool was developed. Results: The developed tool was tested with two test sets. The first test set contains 50 images that are similar to the train set. Hence, 97% detection and classification accuracy has been achieved. The second test set contains 137 completely different real prostate tissue biopsy images, thus, 89% detection accuracy has been achieved. Conclusion: In this study, an automatic prostate cancer detection and diagnosis tool was developed. The test results show that high-accuracy (high-performance) prostate cancer diagnosis tools can be developed using AI (computer vision) methods such as object detection algorithms. These systems can decrease the inter-observer variability among pathologists, and help prevent the time delay in the diagnosis phase.