A deep learning network for Gleason grading of prostate biopsies using EfficientNet
Related Experiment Video
Updated: Aug 23, 2025
Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
Published on: July 11, 2025
Karthik Ramamurthy1, Abinash Reddy Varikuti2, Bhavya Gupta2
1Centre for Cyber Physical Systems, School of Electronics Engineering, Vellore Institute of Technology, Chennai, India.
Objectives:
The most crucial part in the diagnosis of cancer is severity grading. Gleason's score is a widely used grading system for prostate cancer. Manual examination of the microscopic images and grading them is tiresome and consumes a lot of time. Hence to automate the Gleason grading process, a novel deep learning network is proposed in this work.
Methods:
In this work, a deep learning network for Gleason grading of prostate cancer is proposed based on EfficientNet architecture. It applies a compound scaling method to balance the dimensions of the underlying network. Also, an additional attention branch is added to EfficientNet-B7 for precise feature weighting.
Result:
To the best of our knowledge, this is the first work that integrates an additional attention branch with EfficientNet architecture for Gleason grading. The proposed models were trained using H&E-stained samples from prostate cancer Tissue Microarrays (TMAs) in the Harvard Dataverse dataset.
Conclusions:
The proposed network was able to outperform the existing methods and it achieved an Kappa score of 0.5775.
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