Optimized EfficientNet for Detection of Endometrial Cancer Using Histopathological Images

B. T. Keerthishree; Pushpa Ravikumar

doi:10.48084/etasr.13925

Authors

B. T. Keerthishree Department of Computer Science and Engineering, Adichunchanagiri Institute of Technology, Chikkamagaluru, Visvesvaraya Technological University, Karnataka, Belagavi, India
Pushpa Ravikumar Department of Computer Science and Engineering, Adichunchanagiri Institute of Technology, Chikkamagaluru, Visvesvaraya Technological University, Karnataka, Belagavi, India

Volume: 16 | Issue: 1 | Pages: 32120-32126 | February 2026 | https://doi.org/10.48084/etasr.13925

Received: 18 August 2025 | Revised: 12 September 2025, 24 October 2025, and 24 November 2025 | Accepted: 26 November 2025 | Online: 9 February 2026

Corresponding author: B. T. Keerthishree

Abstract

The diagnosis of endometrial cancer is highly dependent on histopathological analysis, which is often time-consuming and subjective. This study introduces an optimized EfficientNet-B7.5DeltaR model for the automatic detection of stromal tissue, blood vessels, and neutrophils in tissue patches. The novelty of this work lies in the extension of compound scaling with a new ????-regularization factor and a widthwise convolution layer, enhancing generalization and reducing overfitting compared to the baseline EfficientNet. The proposed model was trained on 91 H&E-stained whole-slide images of endometrial carcinoma Pipelle biopsies from the Radboud University Medical Center, The Netherlands, and evaluated using standard classification and regression metrics. Results demonstrate a 6% improvement in validation accuracy, a 12% gain in R² score, and a 5% increase in F1-score over the baseline, confirming the improved generalization of the model for histopathological cancer analysis.

Keywords:

EfficientNet, endometrial cancer, histopathological images, stroma, blood vessel, tumor

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