A hybrid lightweight breast cancer classification framework using the histopathological images

• Autorzy: Addo Daniel , Zhou Shijie , Sarpong Kwabena , Nartey Obed T. , Abdullah Muhammed A. , Ukwuoma Chiagoziem C. , Al-antari Mugahed A.

Identyfikatory

DOI

10.1016/j.bbe.2023.12.003

• Języki publikacji: EN

Abstrakty

EN

A crucial element in the diagnosis of breast cancer is the utilization of a classification method that is efficient, lightweight, and precise. Convolutional neural networks (CNNs) have garnered attention as a viable approach for classifying histopathological images. However, deeper and wider models tend to rely on first-order statistics, demanding substantial computational resources and struggling with fixed kernel dimensions that limit encompassing diverse resolution data, thereby degrading the model’s performance during testing. This study introduces BCHI-CovNet, a novel lightweight artificial intelligence (AI) model for histopathological breast image classification. Firstly, a novel multiscale depth-wise separable convolution is proposed. It is introduced to split input tensors into distinct tensor fragments, each subject to unique kernel sizes integrating various kernel sizes within one depth-wise convolution to capture both low- and high-resolution patterns. Secondly, an additional pooling module is introduced to capture extensive second-order statistical information across the channels and spatial dimensions. This module works in tandem with an innovative multi-head self-attention mechanism to capture the long-range pixels contributing significantly to the learning process, yielding distinctive and discriminative features that further enrich representation and introduce pixel diversity during training. These novel designs substantially reduce computational complexities regarding model parameters and FLOPs, which is crucial for resource-constrained medical devices. The outcomes achieved by employing the suggested model on two openly accessible datasets for breast cancer histopathological images reveal noteworthy performance. Specifically, the proposed approach attains high levels of accuracy: 99.15 % at 40× magnification, 99.08 % at 100× magnification, 99.22 % at 200× magnification, and 98.87 % at 400× magnification on the BreaKHis dataset. Additionally, it achieves an accuracy of 99.38 % on the BACH dataset. These results highlight the exceptional effectiveness and practical promise of BCHI-CovNet for the classification of breast cancer histopathological images.

Słowa kluczowe

EN

breast cancer; histopathological image; depth wise separable convolutional network; second order pooling module; visual explainable saliency map

PL

rak piersi; obraz histopatologiczny; sieć konwolucyjna; mapa wyróżnień

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2024
• Tom: Vol. 44, no. 1
• Strony: 31--54
• Opis fizyczny: Bibliogr. 104 poz., rys., tab., wykr.

Twórcy

autor

Addo Daniel

• School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China

autor

Zhou Shijie

• School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China

autor

Sarpong Kwabena

• School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China

autor

Nartey Obed T.

• School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China

autor

Abdullah Muhammed A.

• School of Information and Software Engineering, University of Electronic Science and Technology of China, Chengdu 610054, China

autor

Ukwuoma Chiagoziem C.

• The College of Nuclear Technology and Automation Engineering, Chengdu University of Technology, Sichuan 610059, China

autor

Al-antari Mugahed A.

• Department of Artificial Intelligence, College of Software & Convergence Technology, Daeyang AI Center, Sejong University, Seoul 05006, Republic of Korea

Bibliografia

• [1] Anastasiadi Z, Lianos GD, Ignatiadou E, Harissis HV, Mitsis M. Breast cancer in young women: an overview. Updates Surg 2017;69(3):313-7. https://doi.org/10.1007/s13304-017-0424-1.
• [2] Mambou SJ, Maresova P, Krejcar O, Selamat A, Kuca K. Breast Cancer Detection Using Infrared Thermal Imaging and a Deep Learning Model. Sensors 2018;18(9). https://doi.org/10.3390/s18092799.
• [3] Karthik R, Menaka R, Siddharth MV. Classification of breast cancer from histopathology images using an ensemble of deep multiscale networks. Biocybernet Biomed Eng 2022;42(3):963-76. https://doi.org/10.1016/j. Bbe.2022.07.006.
• [4] Sahu A, Das PK, Meher S. High accuracy hybrid CNN classifiers for breast cancer detection using mammogram and ultrasound datasets. Biomed Signal Process Control 2023:80(104292. https://doi.org/10.1016/j.bspc.2022.104292.
• [5] Zhao Y, Zhang J, Hu D, Qu H, Tian Y, and Cui X, Application of Deep Learning in Histopathology Images of Breast Cancer: A Review. (in eng), Micromachines (Basel) 2022; 13(12): https://doi.org/10.3390/mi13122197.
• [6] Oyelade ON, Ezugwu AE, Venter HS, Mirjalili S, Gandomi AH. Abnormality classification and localization using dual-branch whole-region-based CNN model with histopathological images. Comput Biol Med 2022;149. https://doi.org/ 10.1016/j.compbiomed.2022.105943.
• [7] Nahiduzzaman M, Faruq Goni MO, Robiul Islam M, Sayeed A, Shamim Anower M, Ahsan M, et al. Detection of various lung diseases including COVID-19 using extreme learning machine algorithm based on the features extracted from a lightweight CNN architecture. Biocybernet Biomed Eng 2023;43(3):528-50. https://doi.org/10.1016/j.bbe.2023.06.003.
• [8] Song S, Xia H, Qin Y, Tan Y. BA-Net: Brightness prior guided attention network for colonic polyp segmentation. Biocybernet Biomed Eng 2023. https://doi.org/10.1016/j.bbe.2023.08.001.
• [9] Feng Y, Atabansi CC, Nie J, Liu H, Zhou H, Zhao H, et al. Multi-stage fully convolutional network for precise prostate segmentation in ultrasound images. Biocybernet Biomed Eng 2023;43(3):586-602. https://doi.org/10.1016/j.bbe.2023.08.002.
• [10] Khairandish MO, Sharma M, Jain V, Chatterjee JM, Jhanjhi NZ. A Hybrid CNNSVM Threshold Segmentation Approach for Tumor Detection and Classification of MRI Brain Images. IRBM 2022;43(4):290-9. https://doi.org/10.1016/j.irbm.2021.06.003.
• [11] Allam JP, Samantray S, Sahoo SP, Ari S. A deformable CNN architecture for predicting clinical acceptability of ECG signal. Biocybernet Biomed Eng 2023;43 (1):335-51. https://doi.org/10.1016/j.bbe.2023.01.006.
• [12] Hammad M, Bakrey M, Bakhiet A, Tadeusiewicz R. El-Latif AaA, and Pławiak P, A novel end-to-end deep learning approach for cancer detection based on microscopic medical images. Biocybernet Biomed Eng 2022;42(3):737-48. https://doi.org/10.1016/j.bbe.2022.05.009.
• [13] Samee NA, Atteia G, Meshoul S, Al-Antari MA, Kadah YM. Deep Learning Cascaded Feature Selection Framework for Breast Cancer Classification: Hybrid CNN with Univariate-Based Approach. Mathematics 2022;10(19):3631. https:// doi.org/10.3390/math10193631.
• [14] Al-Hejri AM, Al-Tam RM, Fazea M, Sable AH, Lee S, Al-Antari MA, et al. Ensemble Self-Attention Transformer Encoder for Breast Cancer Diagnosis Using Full-Field Digital X-ray Breast Images. Diagnostics (Basel) 2022;13(1). https://doi.org/ 10.3390/diagnostics13010089.
• [15] Al-Tam RM, Al-Hejri AM, Narangale SM, Samee NA, Mahmoud NF, Al-Masni MA, et al. A Hybrid Workflow of Residual Convolutional Transformer Encoder for Breast Cancer Classification Using Digital X-ray Mammograms. Biomedicines 2022;10(11). https://doi.org/10.3390/biomedicines10112971.
• [16] Lu SY, Wang SH, Zhang YD. BCDNet: An Optimized Deep Network for Ultrasound Breast Cancer Detection. Innov Res BioMed Eng 2023;44(4):100774. https://doi. org/10.1016/j.irbm.2023.100774.
• [17] Aidossov N, Zarikas V, Zhao Y, Mashekova A, Ng EYK, Mukhmetov O, et al. An Integrated Intelligent System for Breast Cancer Detection at Early Stages Using IR Images and Machine Learning Methods with Explainability. SN Computer Science 2023;4(2):184. https://doi.org/10.1007/s42979-022-01536-9.
• [18] Matos JD, Britto ADS, Oliveira LES, and Koerich AL, Double Transfer Learning for Breast Cancer Histopathologic Image Classification. In: Proc 2019 International Joint Conference on Neural Networks (IJCNN);2019 https://doi.org/10.1109/ IJCNN.2019.8852092.
• [19] Kumar A, Singh SK, Saxena S, Lakshmanan K, Sangaiah AK, Chauhan H et al., Deep feature learning for histopathological image classification of canine mammary tumors and human breast cancer. Information Sciences 2020; 508(405-421 https://doi.org/10.1016/j.ins.2019.08.072.
• [20] Gupta V and Bhavsar A, Sequential Modeling of Deep Features for Breast Cancer Histopathological Image Classification. In: Proc 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW);2018 https://doi.org/10.1109/CVPRW.2018.00302.
• [21] Vo DM, Nguyen N-Q, and Lee S-W, Classification of breast cancer histology images using incremental boosting convolution networks. Information Sciences 2019; 482(123-138 https://doi.org/10.1016/j.ins.2018.12.089.
• [22] Liu W, Juhas M, and Zhang Y, Fine-Grained Breast Cancer Classification With Bilinear Convolutional Neural Networks (BCNNs). (in English), Frontiers in Genetics 2020; 11(https://doi.org/10.3389/fgene.2020.547327.
• [23] Saxena S, Shukla S, Gyanchandani M. Breast cancer histopathology image classification using kernelized weighted extreme learning machine. Int J Imaging Syst Technol 2021;31(1):168-79. https://doi.org/10.1002/ima.22465.
• [24] Demir F. DeepBreastNet: A novel and robust approach for automated breast cancer detection from histopathological images. Biocybernet Biomed Eng 2021; 41(3):1123-39. https://doi.org/10.1016/j.bbe.2021.07.004.
• [25] Nanglia S, Ahmad M, Ali Khan F, Jhanjhi NZ. An enhanced Predictive heterogeneous ensemble model for breast cancer prediction. Biomed Signal Process Control 2022:72(103279. https://doi.org/10.1016/j.bspc.2021.103279.
• [26] Zhang J, Wei X, Che C, Zhang Q, Wei X. Breast Cancer Histopathological Image Classification Based on Convolutional Neural Networks. J Med Imag Health Inform 2019;9(4):735-43. https://doi.org/10.1166/jmihi.2019.2648.
• [27] Boumaraf S, Liu X, Wan Y, Zheng Z, Ferkous C, Ma X, et al. Conventional Machine Learning versus Deep Learning for Magnification Dependent Histopathological Breast Cancer Image Classification: A Comparative Study with Visual Explanation. Diagnostics 2021;11(3):528. https://doi.org/10.3390/ diagnostics11030528.
• [28] Benhammou Y, Tabik S, Achchab B, Herrera F. A first study exploring the performance of the state-of-the art CNN model in the problem of breast cancer. In: In: Proc Proceedings of the International Conference on Learning and Optimization Algorithms: Theory and Applications; 2018. https://doi.org/ 10.1145/3230905.3230940.
• [29] Spanhol FA, Oliveira LS, Petitjean C, and Heutte L, Breast cancer histopathological image classification using Convolutional Neural Networks. In: Proc 2016 International Joint Conference on Neural Networks (IJCNN);2016 https://doi.org/10.1109/IJCNN.2016.7727519.
• [30] Chattopadhyay S, Dey A, Singh PK, Oliva D, Cuevas E, Sarkar R, et al. A deep learning model for detection of breast cancer from histopathological images. Comput Biol Med 2022;150. https://doi.org/10.1016/j.compbiomed.2022.106155.
• [31] Budak Ü, Cömert Z, Rashid ZN, Şengür A, Çıbuk M. Computer-aided diagnosis system combining FCN and Bi-LSTM model for efficient breast cancer detection from histopathological images. Appl Soft Comput 2019;85. https://doi.org/ 10.1016/j.asoc.2019.105765.
• [32] Burçak KC, Baykan ÖK, Uğuz H. A new deep convolutional neural network model for classifying breast cancer histopathological images and the hyperparameter optimisation of the proposed model. J Supercomput 2021;77(1):973-89. https:// doi.org/10.1007/s11227-020-03321-y.
• [33] Maleki A, Raahemi M, Nasiri H. Breast cancer diagnosis from histopathology images using deep neural network and XGBoost. Biomed Signal Process Control 2023:86(105152. https://doi.org/10.1016/j.bspc.2023.105152.
• [34] Garg S, Singh P. Transfer Learning Based Lightweight Ensemble Model for Imbalanced Breast Cancer Classification. IEEE/ACM Trans Comput Biol Bioinf 2023;20(2):1529-39. https://doi.org/10.1109/TCBB.2022.3174091.
• [35] Xu C, Yi K, Jiang N, Li X, Zhong M, and Zhang Y, MDFF-Net: A multi-dimensional feature fusion network for breast histopathology image classification. Computers in Biology and Medicine 2023107385 https://doi.org/10.1016/j. Compbiomed.2023.107385.
• [36] Boumaraf S, Liu X, Zheng Z, Ma X, Ferkous C. A new transfer learning based approach to magnification dependent and independent classification of breast cancer in histopathological images. Biomed Signal Process Control 2021;63. https://doi.org/10.1016/j.bspc.2020.102192.
• [37] Patel V, Chaurasia V, Mahadeva R, and Patole SP, GARL-Net: Graph Based Adaptive Regularized Learning Deep Network for Breast Cancer Classification. IEEE Access 2023; 11(9095-9112 https://doi.org/10.1109/ACCESS.2023.3239671.
• [38] Kashyap R. Breast Cancer Histopathological Image Classification Using Stochastic Dilated Residual Ghost Model. Int J Inform Retrieval Research (IJIRR) 2022;12 (1):1-24. https://doi.org/10.4018/IJIRR.289655.
• [39] Zhu C, Song F, Wang Y, Dong H, Guo Y, Liu J. Breast cancer histopathology image classification through assembling multiple compact CNNs. BMC Med Inf Decis Making 2019;19(1):198. https://doi.org/10.1186/s12911-019-0913-x.
• [40] Toğaçar M, Özkurt KB, Ergen B, and Cömert Z, BreastNet: A novel convolutional neural network model through histopathological images for the diagnosis of breast cancer. Physica A: Statistical Mechanics and its Applications 2020; 545 (https://doi.org/10.1016/j.physa.2019.123592.
• [41] Sharma S, Kumar S. The Xception model: A potential feature extractor in breast cancer histology images classification. ICT Express 2022;8(1):101-8. https://doi. org/10.1016/j.icte.2021.11.010.
• [42] Sitaula C, Aryal S. Fusion of whole and part features for the classification of histopathological image of breast tissue. Health Information Science and Systems 2020;8(1):38. https://doi.org/10.1007/s13755-020-00131-7.
• [43] Yang Z, Ran L, Zhang S, Xia Y, and Zhang Y, EMS-Net: Ensemble of Multiscale Convolutional Neural Networks for Classification of Breast Cancer Histology Images. Neurocomputing 2019; 366:46-53 https://doi.org/10.1016/j. Neucom.2019.07.080.
• [44] Sanyal R, Kar D, Sarkar R. Carcinoma Type Classification From High-Resolution Breast Microscopy Images Using a Hybrid Ensemble of Deep Convolutional Features and Gradient Boosting Trees Classifiers. IEEE/ACM Trans Comput Biol Bioinf 2022;19(4):2124-36. https://doi.org/10.1109/TCBB.2021.3071022.
• [45] Mohamed A, Amer E, Noor Eldin S, Khaled J, Hossam M, Elmasry N et al., The Impact of Data processing and Ensemble on Breast Cancer Detection Using Deep Learning. Journal of Computing and Communication 2022; 1(1): 27-37 https:// doi.org/10.21608/jocc.2022.218453.
• [46] Bagchi A, Pramanik P, Sarkar R. A Multi-Stage Approach to Breast Cancer Classification Using Histopathology Images. Diagnostics 2023;13(1):126. https:// doi.org/10.3390/diagnostics13010126.
• [47] Ibraheem AM, Rahouma KH, Hamed HFA. 3PCNNB-Net: Three Parallel CNN Branches for Breast Cancer Classification Through Histopathological Images. J Med Biol Eng 2021;41(4):494-503. https://doi.org/10.1007/s40846-021-00620-4.
• [48] Kallipolitis A, Revelos K, Maglogiannis I. Ensembling EfficientNets for the Classification and Interpretation of Histopathology Images. Algorithms 2021;14 (10):278. https://doi.org/10.3390/a14100278.
• [49] Guleria HV, Luqmani AM, Kothari HD, Phukan P, Patil S, Pareek P et al., Enhancing the Breast Histopathology Image Analysis for Cancer Detection Using Variational Autoencoder. International Journal of Environmental Research and Public Health 2023; 20(5): 4244. [Online]. Available: https://www.mdpi.com/ 1660-4601/20/5/4244.
• [50] Xu J, Xiang L, Liu Q, Gilmore H, Wu J, Tang J, et al. Stacked Sparse Autoencoder (SSAE) for Nuclei Detection on Breast Cancer Histopathology Images. (in eng). IEEE Trans Med Imaging 2016;35(1):119-30. https://doi.org/10.1109/ tmi.2015.2458702.
• [51] Liu M, He Y, Wu M, and Zeng C, Breast Histopathological Image Classification Method Based on Autoencoder and Siamese Framework. Information 2022; 13(3): 107. [Online]. Available: https://www.mdpi.com/2078-2489/13/3/107.
• [52] Krishna S, Suganthi SS, Bhavsar A, Yesodharan J, Krishnamoorthy S. An interpretable decision-support model for breast cancer diagnosis using histopathology images. J Pathol Inform 2023;14. https://doi.org/10.1016/j.jpi.2023.100319.
• [53] Chattopadhyay S, Dey A, Singh PK, Sarkar R. DRDA-Net: Dense residual dualshuffle attention network for breast cancer classification using histopathological images. Comput Biol Med 2022;145. https://doi.org/10.1016/j.compbiomed.2022.105437.
• [54] Li G, Li C, Wu G, Ji D, and Zhang H, Multi-View Attention-Guided Multiple Instance Detection Network for Interpretable Breast Cancer Histopathological Image Diagnosis. IEEE Access 2021; 9:79671-79684 https://doi.org/10.1109/ ACCESS.2021.3084360.
• [55] Zhong Y, Piao Y, Zhang G. Dilated and soft attention-guided convolutional neural network for breast cancer histology images classification. Microsc Res Tech 2022; 85(4):1248-57. https://doi.org/10.1002/jemt.23991.
• [56] Yang H, Kim JY, Kim H, Adhikari SP. Guided Soft Attention Network for Classification of Breast Cancer Histopathology Images. IEEE Trans Med Imaging 2020;39(5):1306-15. https://doi.org/10.1109/TMI.2019.2948026.
• [57] Budak Ü, Güzel AB. Automatic Grading System for Diagnosis of Breast Cancer Exploiting Co-occurrence Shearlet Transform and Histogram Features. Innov Res BioMed Eng 2020;41(2):106-14. https://doi.org/10.1016/j.irbm.2020.02.001.
• [58] Kausar T, Wang M, Idrees M, Lu Y. HWDCNN: Multi-class recognition in breast histopathology with Haar wavelet decomposed image based convolution neural network. Biocybernet Biomed Eng 2019;39(4):967-82. https://doi.org/10.1016/j.bbe.2019.09.003.
• [59] Hao Y, Qiao S, Zhang L, Xu T, Bai Y, Hu H et al., Breast Cancer Histopathological Images Recognition Based on Low Dimensional Three-Channel Features. (in English), Frontiers in Oncology 2021; 11(https://doi.org/10.3389/fonc.2021.657560.
• [60] Ameh Joseph A, Abdullahi M, Junaidu SB, Hassan Ibrahim H, Chiroma H. Improved multi-classification of breast cancer histopathological images using handcrafted features and deep neural network (dense layer). Intell Syst Appl 2022;14. https://doi.org/10.1016/j.iswa.2022.200066.
• [61] Jia L, Shi X, Sun Q, Tang X, Li P. Second-order convolutional networks for iris recognition. Appl Intell 2022;52(10):11273-87. https://doi.org/10.1007/s10489-021-02925-y.
• [62] Hu J, Shen L, and Sun G, Squeeze-and-Excitation Networks. In: Proc 2018 IEEE/ CVF Conference on Computer Vision and Pattern Recognition;2018 https://doi. org/10.1109/CVPR.2018.00745.
• [63] Hou C, Li J, Wang W, Sun L, Zhang J. Second-order asymmetric convolution network for breast cancer histopathology image classification. J Biophotonics 2022;15(5):e202100370.
• [64] Li J, Zhang J, Sun Q, Zhang H, Dong J, Che C et al., Breast Cancer Histopathological Image Classification Based on Deep Second-order Pooling Network. In: Proc 2020 International Joint Conference on Neural Networks (IJCNN);2020 https://doi.org/10.1109/IJCNN48605.2020.9207604.
• [65] Zou Y, Chen S, Che C, Zhang J, Zhang Q. Breast cancer histopathology image classification based on dual-stream high-order network. Biomed Signal Process Control 2022;78. https://doi.org/10.1016/j.bspc.2022.104007.
• [66] Zou Y, Zhang J, Huang S, Liu B. Breast cancer histopathological image classification using attention high-order deep network. Int J Imaging Syst Technol 2022;32(1):266-79. https://doi.org/10.1002/ima.22628.
• [67] Li P, Xie J, Wang Q, and Zuo W, Is Second-Order Information Helpful for LargeScale Visual Recognition? In: Proc 2017 IEEE International Conference on Computer Vision (ICCV);2017 https://doi.org/10.1109/ICCV.2017.228.
• [68] Li P, Xie J, Wang Q, and Gao Z, Towards Faster Training of Global Covariance Pooling Networks by Iterative Matrix Square Root Normalization. In: Proc 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition;2018 https:// doi.org/10.1109/CVPR.2018.00105.
• [69] Lin M, Chen Q, and Yan S, Network in network. arXiv preprint arXiv:1312.4400 2013 https://doi.org/10.48550/arXiv.1312.4400.
• [70] Ionescu C, Vantzos O, and Sminchisescu C, Matrix Backpropagation for Deep Networks with Structured Layers. In: Proc 2015 IEEE International Conference on Computer Vision (ICCV);2015 https://doi.org/10.1109/ICCV.2015.339.
• [71] Lin TY, Roychowdhury A, Maji S. Bilinear Convolutional Neural Networks for Fine-Grained Visual Recognition. IEEE Trans Pattern Anal Mach Intell 2018;40 (6):1309-22. https://doi.org/10.1109/TPAMI.2017.2723400.
• [72] Gao Z, Xie J, Wang Q, and Li P, Global Second-Order Pooling Convolutional Networks. In: Proc 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR);2019 https://doi.org/10.1109/CVPR.2019.00314.
• [73] Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T et al., An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. In: Proc 9th International Conference on Learning Representations, ICLR 2021;2021 https://doi.org/10.48550/arXiv.2010.11929.
• [74] Lee W, Lee H, Lee H, Park EK, Nam H, and Kooi T, Transformer-based Deep Neural Network for Breast Cancer Classification on Digital Breast Tomosynthesis Images. Radiology: Artificial Intelligence 2023; 5(3): e220159 10.1148/ ryai.220159.
• [75] Van Tulder G, Tong Y, and Marchiori E, Multi-view analysis of unregistered medical images using cross-view transformers. In: Proc Medical Image Computing and Computer Assisted Intervention-MICCAI 2021: 24th International Conference, Strasbourg, France, September 27-October 1, 2021, Proceedings, Part III 24;2021.
• [76] Cantone M, Marrocco C, Tortorella F, Bria A. Convolutional networks and transformers for mammography classification: An experimental study. Sensors 2023;23(3):1229.
• [77] Chen X, Zhang K, Abdoli N, Gilley PW, Wang X, Liu H et al., Transformers Improve Breast Cancer Diagnosis from Unregistered Multi-View Mammograms. Diagnostics 2022; 12(7): 1549. [Online]. Available: https://www.mdpi.com/2075-4418/12/7/1549.
• [78] Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN et al., Attention is all you need. In: Proc Advances in neural information processing systems;2017.
• [79] Tomar NK, Srivastava A, Bagci U, and Jha D, Automatic Polyp Segmentation with Multiple Kernel Dilated Convolution Network. In: Proc 2022 IEEE 35th International Symposium on Computer-Based Medical Systems (CBMS);2022 https://doi.org/10.1109/CBMS55023.2022.00063.
• [80] Li M, Li X, Sun W, Wang X, Wang S. Efficient convolutional neural network with multi-kernel enhancement features for real-time facial expression recognition. J Real-Time Image Proc 2021;18(6):2111-22. https://doi.org/10.1007/s11554-021-01088-w.
• [81] Emsawas T, Morita T, Kimura T, Fukui K-I, and Numao M, Multi-Kernel Temporal and Spatial Convolution for EEG-Based Emotion Classification. Sensors 2022; 22 (21): 8250. [Online]. Available: https://www.mdpi.com/1424-8220/22/21/8250.
• [82] Al-Antari MA, Kim T-S. Evaluation of Deep Learning Detection and Classification towards Computer-aided Diagnosis of Breast Lesions in Digital X-ray Mammograms. Comput Methods Programs Biomed 2020. https://doi.org/ 10.1016/j.cmpb.2020.105584.
• [83] Zhao X, Zhang Y, Zhang T, Zou X. Channel Splitting Network for Single MR Image Super-Resolution. IEEE Trans Image Process 2019;28(11):5649-62. https://doi. org/10.1109/TIP.2019.2921882.
• [84] Feng Y, Atabansi CC, Nie J, Liu H, Zhou H, Zhao H, et al. Multi-stage fully convolutional network for precise prostate segmentation in ultrasound images. Biocybernet Biomed Eng 2023. https://doi.org/10.1016/j.bbe.2023.08.002.
• [85] Jurek J, Materka A, Ludwisiak K, Majos A, Gorczewski K, Cepuch K, et al. Supervised denoising of diffusion-weighted magnetic resonance images using a convolutional neural network and transfer learning. Biocybernet Biomed Eng 2023;43(1):206-32. https://doi.org/10.1016/j.bbe.2022.12.006.
• [86] Al-Antari MA, Al-Masni MA, Choi MT, Han SM, and Kim TS, A fully integrated computer-aided diagnosis system for digital X-ray mammograms via deep learning detection, segmentation, and classification. (in eng), Int J Med Inform 2018; 117(44-54 https://doi.org/10.1016/j.ijmedinf.2018.06.003.
• [87] He N, Fang L, Plaza A. Hybrid first and second order attention Unet for building segmentation in remote sensing images. SCIENCE CHINA Inf Sci 2020;63(4): 140305. https://doi.org/10.1007/s11432-019-2791-7.
• [88] Wang Z, Ji S. Second-Order Pooling for Graph Neural Networks. IEEE Trans Pattern Anal Mach Intell 2023;45(6):6870-80. https://doi.org/10.1109/TPAMI.2020.2999032.
• [89] Li H-D, Kallergi M, Clarke LP, Jain VK, Clark RA. Markov random field for tumor detection in digital mammography. IEEE Trans Med Imaging 1995;14(3):565-76.
• [90] Sannasi Chakravarthy SR, Bharanidharan N, Rajaguru H. Deep Learning-Based Metaheuristic Weighted K-Nearest Neighbor Algorithm for the Severity Classification of Breast Cancer. Innov Res BioMed Eng 2023;44(3):100749. https://doi.org/10.1016/j.irbm.2022.100749.
• [91] Wang W, Jiang R, Cui N, Li Q, Yuan F, Xiao Z. Semi-supervised vision transformer with adaptive token sampling for breast cancer classification. Front Pharmacol 2022;13. https://doi.org/10.3389/fphar.2022.929755.
• [92] Yu X, Zhang D, Zhu T, and Jiang X, Novel hybrid multi-head self-attention and multifractal algorithm for non-stationary time series prediction. Information Sciences 2022; 613(541-555 https://doi.org/10.1016/j.ins.2022.08.126.
• [93] Al-Masni MA, Al-Antari MA, Park J-M, Gi G, Kim T-Y, Rivera P et al., Simultaneous detection and classification of breast masses in digital mammograms via a deep learning YOLO-based CAD system. Computer Methods and Programs in Biomedicine 2018; 157(85-94.
• [94] Spanhol FA, Oliveira LS, Petitjean C, Heutte L. A Dataset for Breast Cancer Histopathological Image Classification. IEEE Trans Biomed Eng 2016;63(7): 1455-62. https://doi.org/10.1109/TBME.2015.2496264.
• [95] Koo K-M, Cha E-Y. Image recognition performance enhancements using image normalization. HCIS 2017;7(1):33. https://doi.org/10.1186/s13673-017-0114-5.
• [96] Wieclawek W, Pietka E. Granular filter in medical image noise suppression and edge preservation. Biocybernet Biomed Eng 2019;39(1):1-16. https://doi.org/10.1016/j.bbe.2018.09.006.
• [97] Howard AG, Zhu M, Chen B, Kalenichenko D, Wang W, Weyand T et al., Mobilenets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861 2017 https://doi.org/10.48550/ arXiv.1704.04861.
• [98] Krizhevsky A, Sutskever I, Hinton GE. ImageNet classification with deep convolutional neural networks. Commun ACM 2017;60(6):84-90. https://doi.org/10.1145/3065386.
• [99] Szegedy C, Vanhoucke V, Ioffe S, Shlens J, and Wojna Z, Rethinking the Inception Architecture for Computer Vision. In: Proc 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR);2016 https://doi.org/10.1109/ CVPR.2016.308.
• [100] He K, Zhang X, Ren S, and Sun J, Deep Residual Learning for Image Recognition. In: Proc 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR);2016 https://doi.org/10.1109/CVPR.2016.90.
• [101] Simonyan K and Zisserman A, Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv preprint arXiv:1409.1556v6 2015. [Online]. Available: http://arxiv.org/abs/1409.1556.
• [102] Gupta V, Vasudev M, Doegar A, Sambyal N. Breast cancer detection from histopathology images using modified residual neural networks. Biocybernet Biomed Eng 2021;41(4):1272-87. https://doi.org/10.1016/j.bbe.2021.08.011.
• [103] Ukwuoma CC, Cai D, Heyat MBB, Bamisile O, Adun H, Al-Huda Z, et al. Deep learning framework for rapid and accurate respiratory COVID-19 prediction using chest X-ray images. J King Saud Univ - Comput Inform Sci 2023;35(7):101596. https://doi.org/10.1016/j.jksuci.2023.101596.
• [104] Ukwuoma CC, Qin Z, Belal Bin Heyat M, Akhtar F, Bamisile O, Muaad AY et al., A hybrid explainable ensemble transformer encoder for pneumonia identification from chest X-ray images. J Adv Res 2023; 48:191-211 https://doi.org/10.1016/j. jare.2022.08.021.