E2-MCLNet: An error- and evidence-aware multiscale learning framework for reliable breast cancer histopathology classification

¹ Department of Computer Science and Engineering, School of Engineering and Applied Technology, Bharatiya Engineering Science & Technology Innovation University, Gorantla, Sri Sathya Sai District, Andhra Pradesh, India

² Department of Computer Science and Engineering, KG Reddy College of Engineering & Technology, Chilkur, Moinabad, Rangareddy, Telangana, India

IJOSI, 026210105 https://doi.org/10.6977/IJoSI.202607_10(4).026210105

Received: 18 May 2026 | Revised: 16 June 2026 | Accepted: 4 July 2026 | Published online: 28 July 2026

(This article belongs to the Special Issue Emerging Systematic Innovation Tools and Techniques for Smart Health and Education)

© 2026 by the Author(s). This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution -Noncommercial 4.0 International License (CC-by the license) ( https://creativecommons.org/licenses/by-nc/4.0/ )

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Abstract

Accurate breast histopathology classification is essential for reliable diagnosis; however, tissue heterogeneity, staining variation, and magnification-dependent morphology make automated classification challenging. This study proposes E²-MCLNet, an error- and evidence-aware multiscale learning framework for binary breast histopathology classification on the BreaKHis dataset. The model uses a pretrained ResNet-34 backbone to extract intermediate and deep multiscale features, fuses adaptively pooled descriptors, and applies a Softplus-based evidential classification head with a custom ErrorEvidenceLoss to emphasize difficult and error-prone samples. Using a fixed 70:15:15 train–validation–test split, E²-MCLNet achieved an accuracy of 97.6411%, a precision of 98.0296%, a recall of 98.5149%, a specificity of 95.7784%, an F1-score of 98.2716%, and an area under the curve of 0.9974. Confusion matrix, receiver operating characteristic/precision–recall, and heatmap analyses indicate that the proposed framework provides strong discriminative performance while supporting evidence-aware and reliability-sensitive prediction.

Keywords

Breast cancer histopathology classification

Multiscale learning

Evidential deep learning

Error-aware learning

Reliability-aware classification

BreaKHis

ResNet-34

Computer-aided diagnosis

Funding

None.

Conflict of interest

The authors declare they have no competing interests.

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