Grad-CAM Enabled Breast Cancer Classification with a 3D Inception-ResNet V2: Empowering Radiologists with Explainable Insights

Talaat, Fatma M.; Gamel, Samah A.; El-Balka, Rana Mohamed; Shehata, Mohamed; ZainEldin, Hanaa

doi:10.3390/cancers16213668

Cited by 73AI in cancer detectionRadiomics and Machine Learning in Medical ImagingExplainable Artificial Intelligence (XAI)

Grad-CAM Enabled Breast Cancer Classification with a 3D Inception-ResNet V2: Empowering Radiologists with Explainable Insights

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Talaat, Fatma M., Gamel, Samah A., El-Balka, Rana Mohamed, Shehata, Mohamed, ZainEldin, Hanaa·2024·Cancers

DOI 10.3390/cancers16213668PMC PMC11544836

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Abstract

Breast cancer (BCa) poses a severe threat to women's health worldwide as it is the most frequently diagnosed type of cancer and the primary cause of death for female patients. The biopsy procedure remains the gold standard for accurate and effective diagnosis of BCa. However, its adverse effects, such as invasiveness, bleeding, infection, and reporting time, keep this procedure as a last resort for diagnosis. A mammogram is considered the routine noninvasive imaging-based procedure for diagnosing BCa, mitigating the need for biopsies; however, it might be prone to subjectivity depending on the radiologist's experience. Therefore, we propose a novel, mammogram image-based BCa explainable AI (BCaXAI) model with a deep learning-based framework for precise, noninvasive, objective, and timely manner diagnosis of BCa. The proposed BCaXAI leverages the Inception-ResNet V2 architecture, where the integration of explainable AI components, such as Grad-CAM, provides radiologists with valuable visual insights into the model's decision-making process, fostering trust and confidence in the AI-based system. Based on using the DDSM and CBIS-DDSM mammogram datasets, BCaXAI achieved exceptional performance, surpassing traditional models such as ResNet50 and VGG16. The model demonstrated superior accuracy (98.53%), recall (98.53%), precision (98.40%), F1-score (98.43%), and AUROC (0.9933), highlighting its effectiveness in distinguishing between benign and malignant cases. These promising results could alleviate the diagnostic subjectivity that might arise as a result of the experience-variability between different radiologists, as well as minimize the need for repetitive biopsy procedures.

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Grad-CAM generates heatmaps showing which regions influenced a model's decision, highlighting specific areas of a mammogram that suggest malignancy or pathology features indicative of aggressive disease.
“For imaging applications, techniques like Grad-CAM generate heatmaps showing which regions influenced a model’s decision, highlighting specific areas of a mammogram that suggest malignancy or pathology features indicative of aggressive disease [158,159,160,161].”
From Artificial Intelligence in Clinical Oncology: From Productivity Enhancement to Creative Discovery (2025) · cites this paper as [159]

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[23]Liu S. Enhancing Breast Cancer Classification Using Transfer ResNet with Lightweight Attention Mechanism arXiv 2023 2308.13150

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Authors: Talaat, F. M., Gamel, S. A., El-Balka, R. M., Shehata, M., & ZainEldin, H.
Affiliations: Kafrelsheikh University (EG)
Horus University – Egypt (EG)
Mansoura University (EG)
University of Louisville (US)
Corresponding author: Shehata, Mohamed
ORCID: Talaat, Fatma M.
Gamel, Samah A.
El-Balka, Rana Mohamed
Shehata, Mohamed
ZainEldin, Hanaa
Venue: Cancers
Publisher: MDPI AG
Published: 2024-10-30
Volume: 16
Issue: 21
Pages: 3668
ISSN: 2072-6694 (ISSN-L)
DOI: 10.3390/cancers16213668
PMC: PMC11544836
OpenAlex: W4403946353
Cited by: 67
References: 26
Citations by year: 2026: 36 · 2025: 30 · 2024: 1
FWCI: 9.87
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (vor)
Locations: Cancers (publishedVersion, cc-by)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
Topics: Physical Sciences › Computer Science › Artificial Intelligence › AI in cancer detection
Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Radiomics and Machine Learning in Medical Imaging
Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
Keywords: breast cancer classification, mammography, Grad-CAM, explainable AI, InceptionResNetV2, 3D convolutional neural networks, DDSM, CBIS-DDSM, radiology decision support

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Grad-CAM Enabled Breast Cancer Classification with a 3D Inception-ResNet V2: Empowering Radiologists with Explainable Insights

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