Reinforcement Squeeze-and-Excitation Learning for Automated Gleason Grading in Prostate Cancer Histopathology

Clicks: 1
ID: 312600
2026
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal

Ranked #47 of 705 articles by views in Journal of Computing & Biomedical Informatics

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 705 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
Proper diagnosis and treatment plans of prostate cancer can only be based on accurate Gleason grading of the prostate cancer using histopathological whole-slide images and manual assessment is likely to be subject to inter-observer variability. The present paper will suggest a new Reinforcement Squeeze-and-Excitation Learning-based Convolutional Neural Network (RIL-SE-CNN) to perform automated Gleason grading. The framework combines reinforcement learning and squeeze-and-excitation blocks to establish dynamically recalibrated channel-wise features and provides an ability to focus diagnostically relevant glandular patterns. Reinforcement feedback is an optimal method of reinforcing the attention mechanism by means of rewarding the discriminative feature representations in heterogeneous tissue regions. The proposed model is tested on two benchmark datasets, PANDA and DiagSet-A, with the help of the typical performance indicators. On the PANDA dataset, the model has an accuracy of 0.9646, precision of 0.9642, recall of 0.9657, F1-score of 0.9642 and Matthew correlation coefficient of 0.9634. The suggested method achieves better results on the DiagSet-A dataset with the following accuracy of 0.9958, precision of 0.9954, recall of 0.9957, F1-score of 0.9955, and MCC of 0.9965. These findings indicate the strength and generalization power of the suggested system in the automated Gleason grading.
Reference Key
imported_1777055174_69ebb5c673a71 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Niraj Shah
Journal Journal of Computing & Biomedical Informatics
Year 2026
DOI
DOI not found
URL
https://jcbi.org/index.php/Main/article/view/1277
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.