ORIGINAL RESEARCH article
Front. Immunol.
Sec. Cancer Immunity and Immunotherapy
Volume 16 - 2025 | doi: 10.3389/fimmu.2025.1658741
This article is part of the Research TopicInnovations in Cancer Imaging and Radiomics through Explainable Artificial IntelligenceView all 4 articles
Explainable AI-enabled hybrid deep learning architecture for Breast Cancer Detection
Provisionally accepted
Puyang Miao1*
Yan Zou2- 1Chifeng songshan Hospital, Chifeng, China
- 2Chifeng University, Chifeng, China
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Breast cancer stands is a leading prevalent and potential fatal infection affecting women worldwide, posing the requirement of a reliable and interpretable diagnostic system. This proposed work introduces a hybrid Deep Learning (DL) framework for that integrates three distinct convolutional neural network (CNN) pre-trained architectures: DENSENET121, Xception and VGG16. The proposed fusion strategy enhances feature representation and classification performance through model integration. To address the DL's black-box nature and promote clinical acceptance, the proposed framework incorporates an explainable artificial intelligence (XAI) component utilizing GradCAM++. GRADCAM++ method highlights the multiple lesions with finer edges from the ultrasound images that leads towards the model's predictions, offering transparency and aiding medical professionals in diagnostic validation. Experimental evaluation on benchmark breast cancer datasets demonstrates improved classification accuracy by approximately 13% compared to individual models, demonstrating high performance of the fusion method. This work highlights importance and benefits of integrating DL fusion with XAI methods to build interpretable, high-performing diagnostic tools for clinical use.
Keywords: breast cancer, Ultrasound analysis, XAI, Deep learning fusion, Interpretable Medical Imaging, CNN, VGG16, DenseNet121
Received: 03 Jul 2025; Accepted: 30 Jul 2025.
Copyright: © 2025 Miao and Zou. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Puyang Miao, Chifeng songshan Hospital, Chifeng, China
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