Enhancing breast cancer detection in mammographic imaging using explainable clinical decision support system and framework

Document Type

Article

Source of Publication

Discover Artificial Intelligence

Publication Date

12-5-2025

Abstract

Breast cancer remains one of the leading causes of mortality among women worldwide, where early and precise detection plays a vital role in improving survival rates and treatment outcomes. However, conventional deep learning approaches often encounter challenges in handling dense mammographic tissues and lack transparency in decision-making, limiting their clinical reliability. To address these limitations, this study introduces TransYOLO-GJO, an explainable and optimized detection framework that integrates transformer-based attention mechanisms into the YOLOv9 architecture and leverages the Golden Jackal Optimization (GJO) algorithm for hyperparameter tuning. The transformer encoder enhances contextual feature extraction, particularly in dense breast regions, while GJO dynamically adjusts critical hyperparameters such as anchor sizes, learning rates, and attention configurations to maximize detection performance. The model was trained and evaluated on the CBIS-DDSM dataset, achieving a mean Average Precision (mAP) of 95.1% and an F1-score of 93.2%, outperforming traditional baseline models. Interpretability was incorporated through Grad-CAM heatmaps and SHAP-based feature attribution, enabling radiologists to visualize the decision-making process. The results confirm that the proposed framework not only enhances diagnostic accuracy but also strengthens trust in AI-assisted clinical decision support systems, making it a promising solution for real-world deployment in breast cancer screening.

ISSN

2731-0809

Publisher

Springer Science and Business Media LLC

Volume

6

Issue

1

Disciplines

Computer Sciences | Medicine and Health Sciences

Keywords

Breast cancer detection, Explainable artificial intelligence (XAI), Golden jackal optimization (GJO), Mammographic imaging, Transformer attention mechanism, YOLOv9

Scopus ID

105027433674

Indexed in Scopus

yes

Open Access

yes

Open Access Type

Gold: This publication is openly available in an open access journal/series

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