CUTTING-EDGE CNN APPROACHES FOR BREAST HISTOPATHOLOGICAL CLASSIFICATION: THE IMPACT OF SPATIAL ATTENTION MECHANISMS

Alaa Hussein Abdulaal; Riyam Ali Yassin; Morteza Valizadeh; Ali H. Abdulwahhab; Ali M. Jasim; Ali Jasim Mohammed; Hussein Jumma Jabir; Baraa M. Albaker; Nooruldeen Haider Dheyaa; Mehdi Chehel Amirani

doi:10.29121/shodhai.v1.i1.2024.14

Authors

Alaa Hussein Abdulaal Department of Electrical Engineering, Al-Iraqia University, Baghdad, Iraq
Riyam Ali Yassin Department of Electrical Engineering, Urmia University, West Azerbaijan, Iran
Morteza Valizadeh Department of Electrical Engineering, Urmia University, West Azerbaijan, Iran
Ali H. Abdulwahhab Department of Electrical and Computer Engineering, Altinbas University, Istanbul, Turkey
Ali M. Jasim Department of Network Engineering, Al-Iraqia University, Baghdad, Iraq
Ali Jasim Mohammed Department of Electrical Engineering, Al-Iraqia University, Baghdad, Iraq
Hussein Jumma Jabir Department of Electrical Engineering, Al-Iraqia University, Baghdad, Iraq
Baraa M. Albaker Department of Electrical Engineering, Al-Iraqia University, Baghdad, Iraq
Nooruldeen Haider Dheyaa Department of Communication Engineering, Kashan University, Kashan, Iran
Mehdi Chehel Amirani Department of Electrical Engineering, Urmia University, West Azerbaijan, Iran

DOI:

https://doi.org/10.29121/shodhai.v1.i1.2024.14

Keywords:

Breast Cancer, CAD, Convolutional Neural Network, Spatial Attention Mechanisms, Histopathological Images

Abstract

This paper investigates advanced techniques for breast Histopathological Classification using two robust convolutional neural network (CNN) architectures: Inception V3 and VGG19. Spatial attention mechanisms are integrated to enhance the models' capability to focus on crucial regions within histology images. These enhancements improve diagnostic accuracy by allowing the models to concentrate on critical features for accurate detection. The research leverages two prominent datasets, BACH for multiclass classification and BreaKHis for binary classification, which provides extensive collections of breast cancer histology images, enabling thorough training and evaluation of the proposed models. InceptionV3 with spatial attention mechanism achieved an accuracy of 99.73% for binary classification and 99.06% for multiclass classification. Integrating spatial attention mechanisms is anticipated to significantly advance the development of automated breast cancer detection systems, offering potential improvements in early diagnosis and treatment planning. This study demonstrates how combining state-of-the-art CNN architectures with attention mechanisms can significantly improve medical image analysis, ultimately contributing to better patient outcomes.

References

Abdulaal, A. H., Dheyaa, N. H., Abdulwahhab, A. H., Yassin, R. A., Valizadeh, M., Albaker, B. M., & Mustaf, A. S. (2024). Deep Learning-based Signal Identification in Wireless Communication Systems: a Comparative Analysis on 3G, LTE, and 5G Standards. Al-Iraqia Journal for Scientific Engineering Research , 3(3), 60-70. https://doi.org/10.58564/IJSER.3.3.2024.224

Abdulaal, A. H., Valizadeh, M., AlBaker, B. M., Yassin, R. A., Amirani, M. C., & Shah, A. F. M. S. (2024). Enhancing Breast Cancer Classification Using a Modified GoogLeNet Architecture with Attention Mechanism. Al-Iraqia Journal of Scientific Engineering Research, 3(1). https://doi.org/10.58564/ijser.3.1.2024.145

Abdulaal, A. H., Valizadeh, M., Amirani, M. C., & Shahen Shah, A. F. M. (2024). A self-learning Deep Neural Network for Classification of Breast Histopathological Images. Biomedical Signal Processing and Control, 87(Part B), 105418. https://doi.org/10.1016/j.bspc.2023.105418

Abdulwahhab, A. H., A. H. Abdulaal, Assad, A. A. Mohammed, & M. Valizadeh. (2024). Detection of Epileptic Seizure Using EEG Signals Analysis Based on Deep Learning Techniques. Chaos, Solitons & Fractals/Chaos, Solitons and Fractals, 181, 114700-114700. https://doi.org/10.1016/j.chaos.2024.114700

Aldakhil, L, H. Alhasson, & Alharbi, S. (2024). Attention-Based Deep Learning Approach for Breast Cancer Histopathological Image Multi-Classification. Diagnostics, 14(13), 1402-1402. https://doi.org/10.3390/diagnostics14131402

Ali, L., Alnajjar, F., Jassmi, H. A., Gocho, M., Khan, W., & Serhani, M. A. (2021). Performance Evaluation of Deep CNN-Based Crack Detection and Localization Techniques for Concrete Structures. Sensors, 21(5), 1688. https://doi.org/10.3390/s21051688

Araújo, T., Aresta, G., Castro, E., Rouco, J., Aguiar, P., Eloy, C., Polónia, A., & Campilho, A. (2017). Classification of Breast Cancer Histology Images Using Convolutional Neural Networks. PLOS ONE, 12(6), e0177544. https://doi.org/10.1371/journal.pone.0177544

Balasubramanian, A. A, Awad, M., Singh, A., Breggia, A., Ahmad, B., Christman, R., Ryan, S. T., & Saeed Amal. (2024). Ensemble Deep Learning-Based Image Classification for Breast Cancer Subtype and Invasiveness Diagnosis from Whole Slide Image Histopathology. Cancers, 16(12), 2222-2222. https://doi.org/10.3390/cancers16122222

Cheng, Z., Qu, A., & He, X. (2021). Contour-aware Semantic Segmentation Network with Spatial Attention Mechanism for Medical Image. The Visual Computer, 38(3), 749-762. https://doi.org/10.1007/s00371-021-02075-9

Das, S., & Fabia, M. (2024). Binary Classification of Breast Tumours Using CBAM-Enhanced Collaborative Network. 2024 3rd International Conference on Advancement in Electrical and Electronic Engineering (ICAEEE), 1-6. https://doi.org/10.1109/icaeee62219.2024.10561843

Ding, Q., Shao, Z., Huang, X., & Altan, O. (2021). DSA-Net: a Novel Deeply Supervised attention-guided Network for Building Change Detection in high-resolution Remote Sensing Images. International Journal of Applied Earth Observation and Geoinformation, 105, 102591. https://doi.org/10.1016/j.jag.2021.102591

Elmore, J. G., Longton, G. M., Carney, P. A., Geller, B. M., Onega, T., Tosteson, A. N. A., Nelson, H. D., Pepe, M. S., Allison, K. H., Schnitt, S. J., O'Malley, F. P., & Weaver, D. L. (2015). Diagnostic Concordance among Pathologists Interpreting Breast Biopsy Specimens. JAMA, 313(11), 1122-1132. https://doi.org/10.1001/jama.2015.1405

Giaquinto, A. N., Sung, H., Miller, K. D., Kramer, J. L., Newman, L. A., Minihan, A., Jemal, A., & Siegel, R. L. (2022). Breast Cancer Statistics, 2022. CA: A Cancer Journal for Clinicians, 72(6). https://doi.org/10.3322/caac.21754

Guo, D., Lin, Y., Ji, K., Han, L., Liao, Y., Shen, Z., Feng, J., & Tang, M. (2024). Classify Breast Cancer Pathological Tissue Images Using Multi-Scale Bar Convolution Pooling Structure with Patch Attention. Biomedical Signal Processing and Control, 96, 106607-106607. https://doi.org/10.1016/j.bspc.2024.106607

Guo, M.-H., Xu, T.-X., Liu, J.-J., Liu, Z.-N., Jiang, P.-T., Mu, T.-J., Zhang, S.-H., Martin, R. R., Cheng, M.-M., & Hu, S.-M. (2022). Attention Mechanisms in Computer vision: a Survey. Computational Visual Media, 8(3), 331-368. https://doi.org/10.1007/s41095-022-0271-y

Huang, S., Yang, J., Fong, S., & Zhao, Q. (2019). Artificial Intelligence in Cancer Diagnosis and prognosis: Opportunities and Challenges. Cancer Letters, 471(0304-3835). https://doi.org/10.1016/j.canlet.2019.12.007

Jadoon, E. K., F.G. Khan, S. A.Shah, Khan, A., & ElAffendi, M. (2023). Deep Learning-Based Multi-Modal Ensemble Classification Approach for Human Breast Cancer Prognosis. IEEE Access, 1-1. https://doi.org/10.1109/access.2023.3304242

Jasti, V. D. P., Zamani, A. S., Arumugam, K., Naved, M., Pallathadka, H., Sammy, F., Raghuvanshi, A., & Kaliyaperumal, K. (2022). Computational Technique Based on Machine Learning and Image Processing for Medical Image Analysis of Breast Cancer Diagnosis. Security and Communication Networks, 2022, e1918379. https://doi.org/10.1155/2022/1918379

Kausar, T., Lu, Y., & Kausar, A. (2023). Breast Cancer Diagnosis Using Lightweight Deep Convolution Neural Network Model. IEEE Access, 11, 124869-124886. https://doi.org/10.1109/access.2023.3326478

Litjens, G., Kooi, T., Bejnordi, B. E., Setio, A. A. A., Ciompi, F., Ghafoorian, M., van der Laak, J. A. W. M., van Ginneken, B., & Sánchez, C. I. (2017). A Survey on Deep Learning in Medical Image Analysis. Medical Image Analysis, 42(1), 60-88. https://doi.org/10.1016/j.media.2017.07.005

Liu, L., Wang, Y., Zhang, P., Qiao, H., Sun, T., Zhang, H., Xu, X., & Shang, H. (2024). Collaborative Transfer Network for Multi-Classification of Breast Cancer Histopathological Images. IEEE Journal of Biomedical and Health Informatics, 28(1), 110-121. https://doi.org/10.1109/jbhi.2023.3283042

Liu, Y., Chen, P.-H. C., Krause, J., & Peng, L. (2019). How to Read Articles That Use Machine Learning : Users' Guides to the Medical Literature. JAMA, 322(18), 1806-1816. https://doi.org/10.1001/jama.2019.16489

McKinney, S. M., Sieniek, M., Godbole, V., Godwin, J., Antropova, N., Ashrafian, H., Back, T., Chesus, M., Corrado, G. C., Darzi, A., Etemadi, M., Garcia-Vicente, F., Gilbert, F. J., Halling-Brown, M., Hassabis, D., Jansen, S., Karthikesalingam, A., Kelly, C. J., King, D., & Ledsam, J. R. (2020). International Evaluation of an AI System for Breast Cancer Screening. Nature, 577(7788), 89-94. https://doi.org/10.1038/s41586-019-1799-6

Moscalu, M, Moscalu, R., C. G. Dascălu, V. Ţarcă, Cojocaru, E., Costin, I., Țarcă, E., & I. L. Șerban. (2023). Histopathological Images Analysis and Predictive Modeling Implemented in Digital Pathology-Current Affairs and Perspectives. Diagnostics, 13(14), 2379-2379. https://doi.org/10.3390/diagnostics13142379

Raaj, R. S. (2023). Breast Cancer Detection and Diagnosis Using Hybrid Deep Learning Architecture. Biomedical Signal Processing and Control, 82, 104558. https://doi.org/10.1016/j.bspc.2022.104558

Rahman, M., Deb, K., P. K. Dhar, & Shimamura, T. (2024). ADBNet: an Attention-Guided Deep Broad Convolutional Neural Network for the Classification of Breast Cancer Histopathology Images. IEEE Access, 1-1. https://doi.org/10.1109/access.2024.3419004

Ritesh Maurya, Nageshwar Nath Pandey, Malay Kishore Dutta, & Mohan Karnati. (2024). FCCS-Net: Breast Cancer Classification Using Multi-Level Fully Convolutional-Channel and Spatial attention-based Transfer Learning Approach. Biomedical Signal Processing and Control, 94, 106258-106258. https://doi.org/10.1016/j.bspc.2024.106258

Sharma, S., Mehra, R., & Kumar, S. (2020). Optimised CNN in Conjunction with Efficient Pooling Strategy for the Multi‐classification of Breast Cancer. IET Image Processing, 15(4), 936-946. https://doi.org/10.1049/ipr2.12074

uder, E., McBride, R., & Sieh, W. (2019). Deep Learning to Improve Breast CancerShen, L., Margolies, L. R., Rothstein, J. H., Fl Detection on Screening Mammography. Scientific Reports, 9(1). https://doi.org/10.1038/s41598-019-48995-4

Simonyan, K., & Zisserman, A. (2014). Very Deep Convolutional Networks for large-scale Image Recognition‏. ArXiv Preprint ArXiv:1409.1556 .

Spanhol, F. A., Oliveira, L. S., Petitjean, C., & Heutte, L. (2016). A Dataset for Breast Cancer Histopathological Image Classification. IEEE Transactions on Biomedical Engineering, 63(7), 1455-1462. https://doi.org/10.1109/tbme.2015.2496264

Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., & Wojna, Z. (2016). Rethinking the Inception Architecture for Computer Vision. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2818-2826. https://doi.org/10.1109/CVPR.2016.308

Ukwuoma, C. C., Hossain, M. A., Jackson, J. K., Nneji, G. U., Monday, H. N., & Qin, Z. (2022). Multi-Classification of Breast Cancer Lesions in Histopathological Images Using DEEP_Pachi: Multiple Self-Attention Head. Diagnostics, 12(5), 1152. https://doi.org/10.3390/diagnostics12051152

Xu, C., Yi, K., Jiang, N., Li, X., Zhong, M., & Zhang, Y. (2023). MDFF-Net: a multi-dimensional Feature Fusion Network for Breast Histopathology Image Classification. Computers in Biology and Medicine, 165, 107385. https://doi.org/10.1016/j.compbiomed.2023.107385

Yan, J., Peng, Z., Yin, H., Wang, J., Wang, X., Shen, Y., Stechele, W., & Cremers, D. (2020). Trajectory Prediction for Intelligent Vehicles Using Spatial‐attention Mechanism. IET Intelligent Transport Systems, 14(13), 1855-1863. https://doi.org/10.1049/iet-its.2020.0274

Zhou, Y., Zhang, C., & Gao, S. (2022). Breast Cancer Classification from Histopathological Images Using Resolution Adaptive Network. IEEE Access, 10, 35977-35991. https://doi.org/10.1109/access.2022.3163822

Zou, Y., Zhang, J., Huang, S., & Liu, B. (2021). Breast Cancer Histopathological Image Classification Using Attention High‐order Deep Network‏. International Journal of Imaging Systems and Technology, 32(1), 266-279. https://doi.org/10.1002/ima.22628