BreastHybridNet: A Hybrid Deep Learning Framework for Breast Cancer Diagnosis Using Mammogram Images
DOI:
https://doi.org/10.22399/ijcesen.812Keywords:
Breast Cancer Diagnosis, Hybrid Deep Learning, Mammogram Analysis, Artificial Intelligence in HealthcareAbstract
As a common malignancy in females, breast cancer represents one of the most serious threats to a female's life, which is also closely associated with the Sustainable Development Goal 3 (SDG 3) of the United Nations for keeping healthy lives and promoting the well-being of all people. Breast cancer accounts for the highest number of cancer mortality for females, and early diagnosis is key to reducing disease-specific mortality and mortality in general. Current methods struggle to accurately localize important regions, model sequential dependencies, or combine different features despite considerable improvements in artificial intelligence and deep learning domains. They prevent diagnostic frameworks from being reliable and scalable, especially in low-resourced healthcare settings. This study proposes a novel hybrid deep learning framework, BreastHybridNet, using mammogram images to tackle these mutual challenges. The proposed framework combines a pre-trained CNN backbone for feature extraction, a spatial attention mechanism to automatically highlight the image area, which contains signature patterns carrying diagnostic information, a BiLSTM layer to obtain sequential dependencies of diagnostic features, and a feature fusion strategy to process complementarily. Experimental results show that the accuracy of the proposed model is 98.30%, which outperforms the state-of-the-art methods LMHistNet, BreastMultiNet, and DOTNet 2.0 to a considerable extent quantitatively. BreastHybridNet works towards the feasibility of interpretability and scalability on existing systems while contributing to worldwide efforts to alleviate cancer-related mortality using cost-efficient diagnostic lenses. This study highlights the need for AI-enabled solutions to contribute to accessing reliable healthcare technologies for breast cancer screening.
References
SOUMYA SARA KOSHY AND L. JANI ANBARASI. (2024). LMHistNet: Levenberg–Marquardt Based Deep Neural Network for Classification of Breast Cancer Histopathological Images. IEEE Access. 12,52051-52066. DOI:10.1109/ACCESS.2024.3385011 DOI: https://doi.org/10.1109/ACCESS.2024.3385011
Luyang Luo, Xi Wang, Yi Lin, Xiaoqi Ma, Andong Tan, Ronald Chan, Varut Vardhanabhuti, Winnie CW Chu, Kwang-Ting Cheng, and Hao Chen. (2024). Deep Learning in Breast Cancer Imaging: A Decade of Progress and Future Directions. IEEE Access, pp.1-39. DOI:10.1109/RBME.2024.3357877 DOI: https://doi.org/10.1109/RBME.2024.3357877
Soham Saha, Ahona Dutta and Sabarna Choudhury. (2024). A Deep-Learning-Based Novel Method to Classify Breast Cancer. IEEE Access, pp.1-6. DOI:10.1109/IDCIoT59759.2024.10467223 DOI: https://doi.org/10.1109/IDCIoT59759.2024.10467223
Zhen Yu Gordon Ko, Yang Li, Jiulong Liu, Hui Ji, Anqi Qiu, Nanguang Chen. (2024). DOTnet 2.0: Deep learning network for diffuse optical tomography image reconstruction. Intelligence-Based Medicine 9, 100133 https://doi.org/10.1016/j.ibmed.2023.100133 DOI: https://doi.org/10.1016/j.ibmed.2023.100133
Muhammad anas, ihtisham ul haq, ghassan husnain, and syed ali faraz jaffery. (2024). Advancing Breast Cancer Detection: Enhancing YOLOv5 Network for Accurate Classification in Mammogram Images. IEEE Access. 12, pp.16474 - 16488. DOI:10.1109/ACCESS.2024.3358686 DOI: https://doi.org/10.1109/ACCESS.2024.3358686
Ling Tan, Ying Liang, Jingming Xia, Hui Wu, and Jining Zhu. (2024). Detection and Diagnosis of Small Target Breast Masses Based on Convolutional Neural Networks. IEEE Access. 29(5), pp.1524 - 1539. DOI:10.26599/TST.2023.9010126 DOI: https://doi.org/10.26599/TST.2023.9010126
Awotunde, J.B., Panigrahi, R., Khandelwal, B. et al. (2023). Breast cancer diagnosis based on hybrid rule-based feature selection with deep learning algorithm. Res. Biomed. Eng. 39, 115–127 https://doi.org/10.1007/s42600-022-00255-7 DOI: https://doi.org/10.1007/s42600-022-00255-7
Selina sharmin, tanvir ahammad, md. alamin talukder, and partho ghose. (2023). A hybrid dependable deep feature extraction and ensemble-based machine learning approach for breast cancer detection. IEEE Access. 11, pp.87694 - 87708. DOI:10.1109/ACCESS.2023.3304628 DOI: https://doi.org/10.1109/ACCESS.2023.3304628
Abhisheka, B., Biswas, S.K. & Purkayastha, B. (2023). A Comprehensive Review on Breast Cancer Detection, Classification and Segmentation Using Deep Learning. Arch Computat Methods Eng 30, 5023–5052 https://doi.org/10.1007/s11831-023-09968-z DOI: https://doi.org/10.1007/s11831-023-09968-z
Bita Asadi and Qurban Memon. (2023). Efficient breast cancer detection via cascade deep learning network. Elsevier. 4, pp.46-52. https://doi.org/10.1016/j.ijin.2023.02.001 DOI: https://doi.org/10.1016/j.ijin.2023.02.001
Gami, B., Chauhan, K., Panchal, B.Y. (2023). Breast Cancer Detection Using Deep Learning. In: Marriwala, N., Tripathi, C., Jain, S., Kumar, D. (eds) Mobile Radio Communications and 5G Networks. Lecture Notes in Networks and Systems, vol 588. Springer, Singapore. https://doi.org/10.1007/978-981-19-7982-8_8 DOI: https://doi.org/10.1007/978-981-19-7982-8_8
Steven J. Frank. (2023). A deep learning architecture with an object-detection algorithm and a convolutional neural network for breast mass detection and visualization. Elsevier. 3, pp.1-7. Healthcare Analytics 3,100186 https://doi.org/10.1016/j.health.2023.100186 DOI: https://doi.org/10.1016/j.health.2023.100186
Dewangan, K.K., Dewangan, D.K., Sahu, S.P. et al. (2022). Breast cancer diagnosis in an early stage using novel deep learning with hybrid optimization technique. Multimed Tools Appl 81, 13935–13960 https://doi.org/10.1007/s11042-022-12385-2 DOI: https://doi.org/10.1007/s11042-022-12385-2
Balaha, H. M., Saif, M., Tamer, A., & Abdelhay, E. H. (2022). Hybrid deep learning and genetic algorithms approach (HMB-DLGAHA) for the early ultrasound diagnoses of breast cancer. Neural Computing and Applications. 34, 8671–8695 https://doi.org/10.1007/s00521-021-06851-5 DOI: https://doi.org/10.1007/s00521-021-06851-5
Saikat Islam Khan, Ashef Shahrior, Razaul Karim, Mahmodul Hasan and Anichur Rahman. (2022). MultiNet: A deep neural network approach for detecting breast cancer through multi-scale feature fusion. Journal of King Saud University - Computer and Information Sciences 34(8);6217-6228 https://doi.org/10.1016/j.jksuci.2021.08.004 DOI: https://doi.org/10.1016/j.jksuci.2021.08.004
Ali Bou Nassif, Manar Abu Talib, Qassim Nasir, Yaman Afadar, Omar Elgendy. (2022). Breast cancer detection using artificial intelligence techniques: A systematic literature review. Artificial Intelligence in Medicine 127, 102276 https://doi.org/10.1016/j.artmed.2022.102276 DOI: https://doi.org/10.1016/j.artmed.2022.102276
Md. Mahbubur Rahman, Md. Saikat Islam Khan and Hafiz Md. Hasan Babu. (2022). BreastMultiNet: A multi-scale feature fusion method using deep neural network to detect breast cancer. Array 16,100256 https://doi.org/10.1016/j.array.2022.100256 DOI: https://doi.org/10.1016/j.array.2022.100256
Hana Mechria, Khaled Hassine and Mohamed Salah Gouider. (2022). Breast cancer detection in mammograms using deep learning. Elsevier. 207, pp.2345-2352. Procedia Computer Science 207, 2345-2352 https://doi.org/10.1016/j.procs.2022.09.293 DOI: https://doi.org/10.1016/j.procs.2022.09.293
Yan, Rui; Ren, Fei; Wang, Zihao; Wang, Lihua; Zhang, Tong; Liu, Yudong; Rao, Xiaosong; Zheng, Chunhou and Zhang, Fa (2019). Breast cancer histopathological image classification using a hybrid deep neural network. Methods, 173;52-60. http://doi:10.1016/j.ymeth.2019.06.014 DOI: https://doi.org/10.1016/j.ymeth.2019.06.014
Aditya Khamparia; Subrato Bharati; Prajoy Podder; Deepak Gupta; Ashish Khanna; Thai Kim Phung and Dang N. H. Thanh; (2021). Diagnosis of breast cancer based on modern mammography using hybrid transfer learning. Multidimensional Systems and Signal Processing. 32, 747–765 http://doi:10.1007/s11045-020-00756-7 DOI: https://doi.org/10.1007/s11045-020-00756-7
Krithiga, R. and Geetha, P.(2020). Deep learning based breast cancer detection and classification using fuzzy merging techniques. Machine Vision and Applications, 31(7-8), 63–. http://doi:10.1007/s00138-020-01122-0 DOI: https://doi.org/10.1007/s00138-020-01122-0
Jitendra V. Tembhurne; Anupama Hazarika and Tausif Diwan;. (2021). BrC-MCDLM: breast Cancer detection using Multi-Channel deep learning model. Multimedia Tools and Applications. 80, 31647–31670 http://doi:10.1007/s11042-021-11199-y DOI: https://doi.org/10.1007/s11042-021-11199-y
Jahangeer, Gul Shaira Banu and Rajkumar, T. Dhiliphan . (2020). Early detection of breast cancer using hybrid of series network and VGG-16. Multimedia Tools and Applications. 80, 7853–7886 http://doi:10.1007/s11042-020-09914-2 DOI: https://doi.org/10.1007/s11042-020-09914-2
Abeer Saber; Mohamed Sakr; Osama M. Abo-Seida; Arabi Keshk and Huiling Chen;. (2021). A Novel Deep-Learning Model for Automatic Detection and Classification of Breast Cancer Using the Transfer-Learning Technique. IEEE Access. PP(99):1-1 http://doi:10.1109/access.2021.3079204 DOI: https://doi.org/10.1109/ACCESS.2021.3079204
Yu, Cuiru; Chen, Houjin; Li, Yanfeng; Peng, Yahui; Li, Jupeng and Yang, Fan. (2019). Breast cancer classification in pathological images based on hybrid features. Multimedia Tools and Applications. 78, 21325–21345 http://doi:10.1007/s11042-019-7468-9 DOI: https://doi.org/10.1007/s11042-019-7468-9
Muhammad Sakib Khan Inan; Rizwan Hasan and Fahim Irfan Alam;. (2021). A Hybrid Probabilistic Ensemble based Extreme Gradient Boosting Approach For Breast Cancer Diagnosis . 2021 IEEE 11th Annual Computing and Communication Workshop and Conference (CCWC). http://doi:10.1109/ccwc51732.2021.9376007 DOI: https://doi.org/10.1109/CCWC51732.2021.9376007
Gunjan Chugh; Shailender Kumar and Nanhay Singh;. (2021). Survey on Machine Learning and Deep Learning Applications in Breast Cancer Diagnosis. Cognitive Computation. 13, 1451–1470 http://doi:10.1007/s12559-020-09813-6 DOI: https://doi.org/10.1007/s12559-020-09813-6
Budak, Ümit; Cömert, Zafer; Rashid, Zryan Najat; Şengür, Abdulkadir and Çıbuk, Musa . (2019). Computer-aided diagnosis system combining FCN and Bi-LSTM model for efficient breast cancer detection from histopathological images. Applied Soft Computing, 85, 105765–. http://doi:10.1016/j.asoc.2019.105765 DOI: https://doi.org/10.1016/j.asoc.2019.105765
Yeşim Eroğlu; Muhammed Yildirim and Ahmet Çinar;. (2021). Convolutional Neural Networks based classification of breast ultrasonography images by hybrid method with respect to benign, malignant, and normal using mRMR . Computers in Biology and Medicine. 133,(C) http://doi:10.1016/j.compbiomed.2021.104407 DOI: https://doi.org/10.1016/j.compbiomed.2021.104407
Dawood Alsaedi; Alexander Melnikov; Khalid Muzaffar; Andreas Mandelis and Omar M. Ramahi;. (2022). A Microwave-Thermography Hybrid Technique for Breast Cancer Detection. IEEE Journal of Electromagnetics, RF and Microwaves in Medicine and Biology. http://doi:10.1109/jerm.2021.3072451 DOI: https://doi.org/10.1109/JERM.2021.3072451
Resmini, R., Faria da Silva, L., Medeiros, P. R. T., Araujo, A. S., Muchaluat-Saade, D. C., & Conci, A. (2021). A hybrid methodology for breast screening and cancer diagnosis using thermography. Computers in Biology and Medicine, 135, 104553. http://doi:10.1016/j.compbiomed.2021.104553 DOI: https://doi.org/10.1016/j.compbiomed.2021.104553
Punitha Stephan; Thompson Stephan; Ramani Kannan and Ajith Abraham;. (2021). A hybrid artificial bee colony with whale optimization algorithm for improved breast cancer diagnosis . Neural Computing and Applications. 33,1-25. http://doi:10.1007/s00521-021-05997-6 DOI: https://doi.org/10.1007/s00521-021-05997-6
Yang, Ziqi; Gong, Xun; Guo, Ying and Liu, Wenbin . (2020). A Temporal Sequence Dual-Branch Network for Classifying Hybrid Ultrasound Data of Breast Cancer. IEEE Access, 1–1. http://doi:10.1109/ACCESS.2020.2990683 DOI: https://doi.org/10.1109/ACCESS.2020.2990683
Maroof, Noorulain; Khan, Asifullah; AhmadQureshi, Shahzad; Rehman, Aziz ul; Khalil, Rafiullah Khan and Shim, Seong-O . (2020). Mitosis Detection in Breast Cancer Histopathology Images Using Hybrid Feature Space. Photodiagnosis and Photodynamic Therapy, 101885–. http://doi:10.1016/j.pdpdt.2020.101885 DOI: https://doi.org/10.1016/j.pdpdt.2020.101885
Benhammou, Yassir; Achchab, Boujemâa; Herrera, Francisco and Tabik, Siham . (2019). BreakHis based Breast Cancer Automatic Diagnosis using Deep Learning: Taxonomy, Survey and Insights. Neurocomputing, 375,1-51. http://doi:10.1016/j.neucom.2019.09.044 DOI: https://doi.org/10.1016/j.neucom.2019.09.044
Amin Ul Haq; Jian Ping Li; Abdus Saboor; Jalaluddin Khan; Wang Zhou; Tao Jiang; Mordecai F. Raji and Samad Wali;. (2020). 3DCNN: Three-Layers Deep Convolutional Neural Network Architecture for Breast Cancer Detection using Clinical Image Data. 2020 17th International Computer Conference on Wavelet Active Media Technology and Information Processing (ICCWAMTIP). http://doi:10.1109/iccwamtip51612.2020.9317312 DOI: https://doi.org/10.1109/ICCWAMTIP51612.2020.9317312
Wadhwa, Gitanjali and Kaur, Amandeep . (2020). A Deep CNN Technique for Detection of Breast Cancer Using Histopathology Images. Advanced Computing and Communication Technologies for High Performance Applications (ACCTHPA), Pp.179–185. http://doi:10.1109/accthpa49271.2020.9213192 DOI: https://doi.org/10.1109/ACCTHPA49271.2020.9213192
Liu, Na; Qi, Er-Shi; Xu, Man; Gao, Bo and Liu, Gui-Qiu . (2019). A novel intelligent classification model for breast cancer diagnosis. Information Processing & Management, 56(3),609–623. http://doi:10.1016/j.ipm.2018.10.014 DOI: https://doi.org/10.1016/j.ipm.2018.10.014
Zhang, Yu-Dong; Satapathy, Suresh Chandra; Guttery, David S.; Górriz, Juan Manuel and Wang, Shui-Hua . (2021). Improved Breast Cancer Classification Through Combining Graph Convolutional Network and Convolutional Neural Network. Information Processing & Management, 58(2), 102439–. http://doi:10.1016/j.ipm.2020.102439 DOI: https://doi.org/10.1016/j.ipm.2020.102439
Kadam, Vinod Jagannath; Jadhav, Shivajirao Manikrao and Vijayakumar, K. . (2019). Breast Cancer Diagnosis Using Feature Ensemble Learning Based on Stacked Sparse Autoencoders and Softmax Regression. Journal of Medical Systems, 43(8),1-11. http://doi:10.1007/s10916-019-1397-z DOI: https://doi.org/10.1007/s10916-019-1397-z
Lee, R. S., Gimenez, F., Hoogi, A., Miyake, K. K., Gorovoy, M., & Rubin, D. L. (2017). Curated Breast Imaging Subset of DDSM. The Cancer Imaging Archive. https://doi.org/10.7937/K9/TCIA.2016.7O02S9CY
Machireddy, C., & Chella, S. (2024). Reconfigurable Acceleration of Neural Networks: A Comprehensive Study of FPGA-based Systems. International Journal of Computational and Experimental Science and Engineering, 10(4). https://doi.org/10.22399/ijcesen.559 DOI: https://doi.org/10.22399/ijcesen.559
Noorbhasha Junnu Babu, Vidya Kamma, R. Logesh Babu, J. William Andrews, Tatiraju.V.Rajani Kanth, & J. R. Vasanthi. (2025). Innovative Computational Intelligence Frameworks for Complex Problem Solving and Optimization. International Journal of Computational and Experimental Science and Engineering, 11(1). https://doi.org/10.22399/ijcesen.834 DOI: https://doi.org/10.22399/ijcesen.834
S. Amuthan, & N.C. Senthil Kumar. (2025). Emerging Trends in Deep Learning for Early Alzheimer’s Disease Diagnosis and Classification: A Comprehensive Review. International Journal of Computational and Experimental Science and Engineering, 11(1). https://doi.org/10.22399/ijcesen.739 DOI: https://doi.org/10.22399/ijcesen.739
SHARMA, M., & BENIWAL, S. (2024). Feature Extraction Using Hybrid Approach of VGG19 and GLCM For Optimized Brain Tumor Classification. International Journal of Computational and Experimental Science and Engineering, 10(4). https://doi.org/10.22399/ijcesen.714 DOI: https://doi.org/10.22399/ijcesen.714
Naresh Babu KOSURI, & Suneetha MANNE. (2024). Revolutionizing Facial Recognition: A Dolphin Glowworm Hybrid Approach for Masked and Unmasked Scenarios. International Journal of Computational and Experimental Science and Engineering, 10(4). https://doi.org/10.22399/ijcesen.560 DOI: https://doi.org/10.22399/ijcesen.560
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