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Explainable Hybrid Deep Learning for Automated Diagnosis of Canine Mammary Tumors

Elham Shawky Salama Heba Askr Ashraf Darwish Aboul Ella Hassanien
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Explainable Hybrid Deep Learning for Automated Diagnosis of Canine Mammary Tumors

by Elham Shawky Salama, Heba Askr, Ashraf Darwish, Aboul Ella Hassanien
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International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 187 - Number 85
Year of Publication: 2026
Authors: Elham Shawky Salama, Heba Askr, Ashraf Darwish, Aboul Ella Hassanien
10.5120/ijca2026926396

Elham Shawky Salama, Heba Askr, Ashraf Darwish, Aboul Ella Hassanien . Explainable Hybrid Deep Learning for Automated Diagnosis of Canine Mammary Tumors. International Journal of Computer Applications. 187, 85 ( Feb 2026), 31-43. DOI=10.5120/ijca2026926396

@article{ 10.5120/ijca2026926396,
author = { Elham Shawky Salama, Heba Askr, Ashraf Darwish, Aboul Ella Hassanien },
title = { Explainable Hybrid Deep Learning for Automated Diagnosis of Canine Mammary Tumors },
journal = { International Journal of Computer Applications },
issue_date = { Feb 2026 },
volume = { 187 },
number = { 85 },
month = { Feb },
year = { 2026 },
issn = { 0975-8887 },
pages = { 31-43 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume187/number85/explainable-hybrid-deep-learning-for-automated-diagnosis-of-canine-mammary-tumors/ },
doi = { 10.5120/ijca2026926396 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2026-02-26T16:48:45+05:30
%A Elham Shawky Salama
%A Heba Askr
%A Ashraf Darwish
%A Aboul Ella Hassanien
%T Explainable Hybrid Deep Learning for Automated Diagnosis of Canine Mammary Tumors
%J International Journal of Computer Applications
%@ 0975-8887
%V 187
%N 85
%P 31-43
%D 2026
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Automatic classification of canine mammary tumors (CMT) plays a vital role in ensuring accurate diagnosis, reliable predictive evaluation, and reducing manual intervention in in the diagnostic process. This paper introduces an Explainable Artificial Intelligence (XAI) system for automated CMT classification, which combines the DenseNet201 deep learning (DL) architecture for feature extraction with a Random Forest (RF) classifier to distinguish between benign and malignant tumors in CMT images. The proposed system follows a structured four-phase pipeline: (1) data acquisition and Laplacian filter preprocessing to enhance tumor edges; (2) feature extraction using DenseNet201 and classification with a Random Forest (RF) model; (3) testing and evaluation of the proposed system; and (4) interpretability analysis using the Shapley Additive exPlanations (SHAP) XAI technique. Experimental results demonstrate that the DenseNet201-RF hybrid model achieves an accuracy of 93.9%, surpassing benchmark classifiers while offering interpretability for clinical validation. The proposed system enables accurate automatic classification of canine mammary tumors while integrating SHAP-based XAI for interpretability.

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Index Terms

Computer Science
Information Sciences

Keywords

Canine mammary tumors Deep learning DenseNet201 Explainable artificial intelligence Random Forest SHAP

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