Breast cancer is the most common cancer among women worldwide, posing significant diagnostic challenges. Traditional diagnostic techniques, while foundational, often lack precision and fail to provide clear insights into their decision-making processes. This limitation underscores the need for advanced diagnostic tools that enhance both accuracy and interpretability. This study aims to integrate cutting-edge deep learning algorithms with Gradient-weighted Class Activation Mapping (Grad-CAM) to improve the accuracy and transparency of breast cancer diagnostics through mammographic analysis. We proposed robust approaches using MobileNet, Xception, and DenseNet models, enhanced with Grad-CAM, to analyze mammogram images. This integration facilitates a deeper understanding of model decisions, highlighting critical diagnostic features through visual explanations. The models were rigorously tested on the MIAS dataset to evaluate their diagnostic performance and reliability, achieving a diagnostic accuracy of 94.17%, demonstrating superior performance compared to traditional methods. The findings show significant potential for clinical application, promising to enhance patient outcomes through more accurate and transparent diagnostic practices in oncology.
Siegel, R.L., Miller, K.D., Fuchs, H.E. and Jemal, A. (2022) Cancer Statistics, 2022. CA : A Cancer Journal for Clinicians , 72, 7-33. https://doi.org/10.3322/caac.21708
Lukong, K.E. (2017) Understanding Breast Cancer—The Long and Winding Road. BBA Clinical , 7, 64-77. https://doi.org/10.1016/j.bbacli.2017.01.001
Munro, M.G., Critchley, H.O.D., Broder, M.S. and Fraser, I.S. (2011) FIGO Classification System (PALM‐COEIN) for Causes of Abnormal Uterine Bleeding in Nongravid Women of Reproductive Age. International Journal of Gynecology & Obstetrics , 113, 3-13. https://doi.org/10.1016/j.ijgo.2010.11.011
Sung, H., Ferlay, J., Siegel, R.L., Laversanne, M., Soerjomataram, I., Jemal, A., et al. (2021) Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA : A Cancer Journal for Clinicians , 71, 209-249. https://doi.org/10.3322/caac.21660
Saadatmand, S., Bretveld, R., Siesling, S. and Tilanus-Linthorst, M.M.A. (2015) Influence of Tumour Stage at Breast Cancer Detection on Survival in Modern Times: Population Based Study in 173 797 Patients. BMJ , 351, h4901. https://doi.org/10.1136/bmj.h4901
Yousefi, M., Krzyżak, A. and Suen, C.Y. (2018) Mass Detection in Digital Breast Tomosynthesis Data Using Convolutional Neural Networks and Multiple Instance Learning. Computers in Biology and Medicine , 96, 283-293.
Billah, Md.S., Sarker, M.R., Uddin, Md.S., et al . (2022) Identifying Cancer-Specific Feature Representations in Breast Cancer Histology Images Using Grad-Cam. Computational and Mathematical Methods in Medicine , 2022, 1-14.
Nguyen, M.D., Nguyen, Q.T., Nguyen, H.P., et al . (2023) A Multimodal Deep Learning Approach for Breast Cancer Classification and Lesion Localization with Grad-Cam. Diagnostics , 13, 265.
Sharma, N., Gupta, S. and Singh, A.K. (2023) Grad-CAM Based Explainable AI for Mammographic Mass Classification and Detection of Architectural Distortion. Computer Methods and Programs in Biomedicine , 226, Article ID: 106867.
Zhi, W., Yueng, H.W.F., Chen, Z., Zandavi, S.M., Lu, Z. and Chung, Y.Y. (2017) Using Transfer Learning with Convolutional Neural Networks to Diagnose Breast Cancer from Histopathological Images. Neural Information Processing : 24 th International Conference , ICONIP 2017, Guangzhou, 14-18 November 2017, 669-676. https://doi.org/10.1007/978-3-319-70093-9_71
Thigpen, D., Kappler, A. and Brem, R. (2018) The Role of Ultrasound in Screening Dense Breasts—A Review of the Literature and Practical Solutions for Implementation. Diagnostics , 8, 20.
Vourtsis, A. and Berg, W.A. (2018) Breast Density Implications and Supplemental Screening. European Radiology , 29, 1762-1777. https://doi.org/10.1007/s00330-018-5668-8
Stafford, A.P., Lucy, M. and Willey, S.C. (2021) Workup and Treatment of Nipple Discharge—A Practical Review. Annals of Breast Surgery , 5, Article No. 22.
Mann, R.M., Kuhl, C.K. and Moy, L. (2019) Contrast‐Enhanced MRI for Breast Cancer Screening. Journal of Magnetic Resonance Imaging , 50, 377-390. https://doi.org/10.1002/jmri.26654
Kerin, E.P. and O’Donnell, J.P.M. (2025) Diagnostic Performance of the Second-Generation Wavelia Microwave Breast Imaging System: A Pilot Clinical Investigation. British Journal of Surgery , 112, No. 11. https://doi.org/10.1093/bjs/znaf242
Liu, H., Zhan, H., Sun, D. and Zhang, Y. (2020) Comparison of BSGI, MRI, Mammography, and Ultrasound for the Diagnosis of Breast Lesions and Their Correlations with Specific Molecular Subtypes in Chinese Women. BMC Medical Imaging , 20, Article No. 98. https://doi.org/10.1186/s12880-020-00497-w
Usmani, U.A., Happonen, A. and Watada, J. (2023) Enhancing Medical Diagnosis through Deep Learning and Machine Learning Approaches in Image Analysis. In: Intelligent Systems Conference , Springer, 449-468.
Zhang, J.J., Wang, Y., Zu, C., et al . (2021) Medical Imaging Based Diagnosis through Machine Learning and Data Analysis. In: Pham, T.D., Yan, H., Ashraf, M.W. and Sjöberg, F., Eds., Advances in Artificial Intelligence , Computation , and Data Science : For Medicine and Life Science , Springer, 179-225.
Yu, K.-H., Chang, R.-F. and Lin, C.-J. (2016) Computer-AIDED diagnosis in Medical Imaging. Academic Press.
Puttagunta, M. and Ravi, S. (2021) Medical Image Analysis Based on Deep Learning Approach. Multimedia Tools and Applications , 80, 24365-24398. https://doi.org/10.1007/s11042-021-10707-4
Zhang, H.H. and Qie, Y.F. (2023) Applying Deep Learning to Medical Imaging: A Review. Applied Sciences , 13, 10521.
Zhou, Z., Gotway, M.B. and Liang, J. (2022) Interpreting Medical Images. In: Cohen, T.A., Patel, V.L. and Shortliffe, E.H., Eds., Intelligent Systems in Medicine and Health : The Role of AI , Springer International Publishing, 343-371. https://doi.org/10.1007/978-3-031-09108-7_12
Taylor, A. and McNulty, P.H. (2013) Computer-Aided Diagnosis in Medical Imaging for Improved Patient Care. Journal of Medical Imagi ng , 1, Article ID: 011009.
Johnson, K.B., Wei, W., Weeraratne, D., Frisse, M.E., Misulis, K., Rhee, K., et al. (2020) Precision Medicine, AI, and the Future of Personalized Health Care. Clinical and Translational Science , 14, 86-93. https://doi.org/10.1111/cts.12884
Marini, T.J., Oppenheimer, D.C., Baran, T.M., Rubens, D.J., Dozier, A., Garra, B., et al. (2021) Testing Telediagnostic Right Upper Quadrant Abdominal Ultrasound in Peru: A New Horizon in Expanding Access to Imaging in Rural and Underserved Areas. PLOS ONE , 16, e0255919. https://doi.org/10.1371/journal.pone.0255919
Longo, L., et al. (2020) Explainable Artificial Intelligence: Concepts, Applications, Research Challenges and Visions. International Cross - Domain Con ference for Machine Learni ng an d Knowledge Extracti on , Dublin, 25-28 August 2020, 1-16.
Zhuang, W.M., Chen, C. and Lyu, L.J. (2023) When Foundation Model Meets Federated Learning: Motivations, Challenges, and Future Directions.
Agarwal, R., et al. (2023) Addressing Algorithmic Bias and the Perpetuation of Health Inequities: An AI Bias Aware Framework. Health Policy and Technology , 12, Article ID: 100702.
Thapa, C. and Camtepe, S. (2021) Precision Health Data: Requirements, Challenges and Existing Techniques for Data Security and Privacy. Computers in Biology and Medicine , 129, Article ID: 104130. https://doi.org/10.1016/j.compbiomed.2020.104130
Sharma, S. and Mehra, R. (2020) Conventional Machine Learning and Deep Learning Approach for Multi-Classification of Breast Cancer Histopathology Images—A Comparative Insight. Journal of Digital Imaging , 33, 632-654. https://doi.org/10.1007/s10278-019-00307-y
Hamed, G., Marey, M.A.E., Amin, S.E. and Tolba, M.F. (2020) Deep Learning in Breast Cancer Detection and Classification. In: Hassanien, A.-E., et al. , Eds., Proceedings of the International Conference on Artificial Intelligence and Computer Vision , Springer International Publishing, 322-333. https://doi.org/10.1007/978-3-030-44289-7_30
Tiwari, M., Bharuka, R., Shah, P. and Lokare, R. (2020) Breast Cancer Prediction Using Deep Learning and Machine Learning Techniques.
Ashraf, R., Kiran, I., Mahmood, T., Ur Rehman Butt, A., Razzaq, N. and Farooq, Z. (2020) An Efficient Technique for Skin Cancer Classification Using Deep Learning. 2020 IEEE 23 rd International Multitopic Conference ( INMIC ), Bahawalpur, 5-7 November 2020, 1-5. https://doi.org/10.1109/inmic50486.2020.9318164
Khan, S., Islam, N., Jan, Z., Ud Din, I. and Rodrigues, J.J.P.C. (2019) A Novel Deep Learning Based Framework for the Detection and Classification of Breast Cancer Using Transfer Learning. Pattern Recognition Letters , 125, 1-6. https://doi.org/10.1016/j.patrec.2019.03.022
Mahmood, T., Li, J., Pei, Y., Akhtar, F., Imran, A. and Yaqub, M. (2021) An Automatic Detection and Localization of Mammographic Microcalcifications ROI with Multi-Scale Features Using the Radiomics Analysis Approach. Cancers , 13, Article No. 5916. https://doi.org/10.3390/cancers13235916
Murtaza, G., Shuib, L., Abdul Wahab, A.W., Mujtaba, G., Mujtaba, G., Nweke, H.F., et al. (2019) Deep Learning-Based Breast Cancer Classification through Medical Imaging Modalities: State of the Art and Research Challenges. Artificial Intelligence Review , 53, 1655-1720. https://doi.org/10.1007/s10462-019-09716-5
Mahmood, T., Li, J., Pei, Y. and Akhtar, F. (2021) An Automated In-Depth Feature Learning Algorithm for Breast Abnormality Prognosis and Robust Characterization from Mammography Images Using Deep Transfer Learning. Biology , 10, Article No. 859. https://doi.org/10.3390/biology10090859
Nabi, M.S., Fauzi, M.F.A., Karim, H.B.A. and Cheah, P.L. (2025) Explainable Deep Learning Models for HER2 IHC Scoring in Breast Cancer Diagnosis. Informatics in Medicine Unlocked , 58, Article 101700. https://doi.org/10.1016/j.imu.2025.101700
Chen, Y.T., Wu, J., Huang, L., Li, X.Y. and Chen, Z.Y. (2022) Global Prevalence of Post-Coronavirus Disease 2019 (Covid-19) Condition or Long Covid: A Meta-Analysis and Systematic Review. The Journal of Infectious Diseases , 226, 1593-1607.
Fujita, H. (2020) AI-Based Computer-Aided Diagnosis (AI-CAD): The Latest Review to Read First. Radiological Physics and Technology , 13, 6-19.
Wu, C., Yan, H., Feng, X.F., et al . (2020) Deep Learning for Medical Image Analysis: A Survey. ACM Computing Surveys ( CSUR ), 53, 1-59.
Rajpurkar, P., Irvin, J., Zhu, K., et al . (2017) CheXNet: Radiologist-Level Pneumonia detection on Chest X-Rays with Deep Learning.
Selvaraju, R.R., Cogswell, M., Das, A., Vedantam, R., Parikh, D. and Batra, D. (2017) Grad-CAM: Visual Explanations from Deep Networks via Gradient-Weighted Class Activation Mapping. Proceedings of the IEEE International Conference on Computer Vision (ICCV) , 2017, 618-626. https://doi.org/10.1109/ICCV.2017.74
Moujahid, H., Cherradi, B., Al-Sarem, M., et al . (2022) Combining CNN and Grad-CAM for Covid-19 Disease Prediction and Visual Explanation. Intelligent Automation & Soft Computing , 32, 723-745.
Li, M.X., Zhang, H.B., Zhang, X.L., et al . (2019) MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications.
Tajbakhsh, N., Shin, J.Y., Gurudu, S.R., et al . (2016) Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine-Tuning? IEEE Transactions on Medical Imag ing , 35, 1299-1312.
Kooi, T., van Ginneken, B., Karssemeijer, N. and den Heeten, A. (2017) Discriminating Benign from Malignant Masses in Mammograms with 3D Convolutional Neural Networks. International Journal of Computer Assisted Radi ology and Surgery , 12, 695-704.
Ching, T., Himmelstein, D.S., Beaulieu-Jones, B.K., Kalinin, A.A., Do, B.T., et al . (2018) Opportunities and Obstacles for Deep Learning in Biology and Medicine. Nature Medicine , 24, 1699-1710.
Chougrad, H., Zouaki, H. and Alheyane, O. (2018) Deep Convolutional Neural Networks for Breast Cancer Screening: Transfer Learning with Classical Architectures. Computer Methods and Programs in Biomedicine , 157, 19-30. https://doi.org/10.1016/j.cmpb.2018.01.011
Suckling, J. (1994) The Mammographic Images Analysis Society Digital Mammogram Database. Exerpta Medica , International Congress Series , Volume 1069, 375-378.
Mahmood, T., Li, J., Pei, Y., Akhtar, F., Rehman, M.U. and Wasti, S.H. (2022) Breast Lesions Classifications of Mammographic Images Using a Deep Convolutional Neural Network-Based Approach. PLOS ONE , 17, e0263126. https://doi.org/10.1371/journal.pone.0263126
Mahmood, T., Li, J., Pei, Y., Akhtar, F., Imran, A. and Rehman, K.U. (2020) A Brief Survey on Breast Cancer Diagnostic with Deep Learning Schemes Using Multi-Image Modalities. IEEE Access , 8, 165779-165809. https://doi.org/10.1109/access.2020.3021343
Ganguly, S., Ganguly, A., Mohiuddin, S., Malakar, S. and Sarkar, R. (2022) Vixnet: Vision Transformer with Xception Network for Deepfakes Based Video and Image Forgery Detection. Expert Systems with Applications , 210, Article ID: 118423.
Wang, S., Xu, M., Sun, Y., Jiang, G., Weng, Y., Liu, X., et al. (2022) Improved Single Shot Detection Using Densenet for Tiny Target Detection. Concurrency and Computation : Practice and Experience , 35, e7491. https://doi.org/10.1002/cpe.7491
Dafni Rose, J., VijayaKumar, K., Singh, L. and Sharma, S.K. (2022) Computer-Aided Diagnosis for Breast Cancer Detection and Classification Using Optimal Region Growing Segmentation with Mobilenet Model. Concurrent Engineering , 30, 181-189.
Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., et al. (2020) Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI. Information Fusion , 58, 82-115. https://doi.org/10.1016/j.inffus.2019.12.012
Minh, D., Wang, H.X., Li, Y.F. and Nguyen, T.N. (2022) Explainable Artificial Intelligence: A Comprehensive Review. Artificial Intelligence Review , 55, 3503-3568.
Selvaraju, R.R., Cogswell, M., Das, A., et al . (2017) Grad-Cam: Visual Explanations from Deep Networks via Gradient-Based Localization. Proceedings of the IEEE International Conf erence on Computer Vi sion , Venice, 22-29 October 2017, 618-626.