A Pyramidal CNN-Based Gleason Grading System Using Digitized Prostate Biopsy Specimens

Abstract

Prostate cancer (PC) is the most common cancer, a significant cause of morbidity, and is the second vital cancer that causes death in the US. Early PC detection is one of the major factors in decreasing mortality. We introduce a deep learning (DL) system for automated Gleason system grading (Gleason pattern (GP) and Gleason score (GS)) and grade groups (GG) using whole slide images (WSIs) of the digitized prostate biopsy specimens (PBSs). The DL is a pyramidal convolution neural network (CNN) approach consisting of progressively larger patch-sized shallow CNN to provide hierarchical information features. We used three patches sizes 100×100 (small), 150×150 (median), and 200×200 (large) pixels, so the pyramidal CNN affords us varying contextual features. The small patches give more local information, while the large patches provide global features. The patch-wise classification yields five probabilities representing the GP types from 1 to 5 at each pyramidal level. Then, we get the average for those three levels. We used three metrics to evaluate the GP classification diagnostic: recall, accuracy, and precision. The classification accuracy for the CNNL (large patches) is 0.77, the best among the three CNNs. The GG results are between 50% to 75% for recall. GG's results are highlighted in our DL systems by comparing them with the current work. © 2022 IEEE.

Keywords Classification, Deep Learning, Gleason system, Prostate cancer