Prostate cancer prediction through a hybrid deep learning method applied to histopathological image.

Abstract

Background: Prostate Cancer (PCa) is a severe disease that affects males globally. The Gleason grading system is a widely recognized method for diagnosing the aggressiveness of PCa using histopathological images. This system evaluates prostate tissue to determine the severity of the disease and guide treatment decisions. However, manual analysis of histopathological images requires highly skilled professionals and is time-consuming.

Methods: To address these challenges, deep learning (DL) is utilized, as it has shown promising results in medical image analysis. Although numerous DL networks have been developed for Gleason grading, many existing methods have limitations such as suboptimal accuracy and high computational complexity. The proposed network integrates MobileNet, an Attention Mechanism (AM), and a capsule network. MobileNet efficiently extracts features from images while addressing computational complexity. The AM focuses on selecting the most relevant features, enhancing the accuracy of Gleason grading. Finally, the capsule network classifies the Gleason grades from histopathological images.

Results: The validation of the proposed network used two datasets, PANDA and Gleason-2019. Ablation studies were conducted and evaluated in the proposed architecture. The results highlight the effectiveness of the proposed network.

Conclusions: The proposed network outperformed existing approaches, achieving an accuracy of 98.08% on the PANDA dataset and 97.07% on the Gleason-2019 dataset.