Evolution of histopathological breast cancer images classification using stochastic dilated residual ghost model

Breast cancer detection is a complex problem to solve, and it is a topic that is still being studied. Deep learning-based models aid medical science by helping to classify benign and malignant cancers and saving lives. Breast cancer histopathological image classification (BreakHis) and breast cancer histopathological annotation and diagnosis (BreCaHAD) datasets are used in the proposed model. The study led to the resolution of four essential issues: 1) Addresses the color divergence issue caused by strain normalization during image generation 2) Data augmentation uses several factors like as flip, rotation, shift, resize, and gamma value in order to overcome overfitting concerns caused by a lack of histopathology images. 3) Using the proposed stochastic dilated convolution (SDC) model, able to find missing features and improve tiny and low-level features such as edge, contour, and color correctness. This model effectively solves depth issues with stochastic pooling and max pooling by combining three proposed units: spatial dilation convolution, channel dilation convolution, and dilated convolution to detect missing features and obtain more information on images. Stochastic pooling may choose any value since it is classified as a heterogeneous approach that can determine tiny and large values. When the max pooling and stochastic pooling are combined, a decent feature map is produced, processed by the dilatation unit, detecting cancer cells without adding to the complexity. 4) To correctly identify breast cancer and extract depth characteristics, the stochastic dilated residual ghost (SDRG) model uses the proposed SDC model, a ghost unit, stochastic upsampling, and downsampling units. The proposed feature selection unit employs linear transformations to produce feature maps to show information based on intrinsic characteristics to eliminate redundant or similar features in convolutional neural networks. Convolution and identity mapping are used to construct and retain intrinsic feature mappings in this skip unit while upsampling with stochastic pooling is used to minimize feature dimensions. The proposed model’s performance was assessed with 150,271 augmented and original histopathology images. The proposed method’s findings compare favorably to eight popular approaches. In addition, the suggested model outperforms the competition in terms of accuracy, average precision score, precision, sensitivity, and f1 score. With an accuracy of 98.41% with BreakHis and 98.60% with the BreCaHAD dataset with enhanced images, the proposed method exceeds numerous state-of-the-art methods.

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[1] Pacilè S, Lopez J, Chone P, Bertinotti T, Grouin J et al. Improving Breast Cancer Detection Accuracy of Mammography with the Concurrent Use of an Artificial Intelligence Tool. Radiology: Artificial Intelligence. 2020;2 (6):e190208. doi:10.1148/ryai.2020190208
[2] Perachino M, Arecco L, Martelli V, Lambertini M. Pyrotinib: a new promising targeted agent for human epidermal growth factor receptor 2-positive breast cancer. Translational Breast Cancer Research. 2020;1:11-11. doi:10.21037/tbcr.2020-20-34
[3] Spanhol F, Oliveira L, Petitjean C, Heutte L. A Dataset for Breast Cancer Histopathological Image Classification. IEEE Transactions on Biomedical Engineering. 2016;63 (7):1455-1462. doi:10.1109/tbme.2015.2496264
[4] Breast Cancer Survival Rates: By Stage, Demographics, and More [Internet]. Healthline. 2021 [cited 16 June 2021]. Available from: https://www.healthline.com/health/breast-cancer/survival-facts-statistics
[5] DeSantis C, Ma J, Gaudet M, Newman L, Miller K et al. Breast cancer statistics, 2019. CA: A Cancer Journal for Clinicians. 2019;69 (6):438-451. doi:10.3322/caac.21583
[6] Fathy W, Ghoneim A. A Deep Learning Approach for Breast Cancer Mass Detection. International Journal of Advanced Computer Science and Applications. 2019;10 (1). doi:10.14569/ijacsa.2019.0100123
[7] LeCun Y, Bengio Y, Hinton G. Deep learning. Nature. 2015;521 (7553):436-444. doi:10.1038/nature14539
[8] Spanhol F, Oliveira L, Petitjean C, Heutte L. Breast cancer histopathological image classification using convolutional neural networks. International Joint Conference Neural Network. 2016. p. 2560–2567.
[9] Bayramoglu N, Kannala J, Heikkilä J. Deep learning for magnification independent breast cancer histopathology image classification. 23rd International Conference on Pattern Recognition (ICPR). 2016; 2440-2445. doi: 10.1109/ICPR.2016.7900002
[10] Spanhol F, Oliveira L, Cavalin P, Petitjean C, Heutte L. Deep features for breast cancer histopathological image classification. IEEE International Conference on Systems, Man, and Cybernetics. 2017; 1868–1873.
[11] Wei B, Han Z, He X, Yin Y. Deep learning model-based breast cancer histopathological image classification. 2nd IEEE International Conference on Cloud Computing and Big Data Analysis (ICCCBD 2017). 2017; 348–353.
[12] Chen H, Dou Q, Wang X, Qin J, Heng P. Mitosis detection in breast cancer histology images via deep cascaded networks. Thirtieth AAAI Conference on Artificial Intelligence (AAAI-16). 2021; 1160–1166.
[13] Xie J, Liu R, Luttrell J, Zhang C. Deep Learning Based Analysis of Histopathological Images of Breast Cancer. Frontiers in Genetics. 2019;10.
[14] Monaco J, Hipp J, Lucas D, Smith S, Balis U et al. Image Segmentation with Implicit Color Standardization Using Spatially Constrained Expectation Maximization: Detection of Nuclei. Medical Image Computing and ComputerAssisted Intervention – MICCAI 2012. 2012;:365-372.
[15] Liu F, Yang L. A Novel Cell Detection Method Using Deep Convolutional Neural Network and Maximum-Weight Independent Set. Lecture Notes in Computer Science. 2015;349-357. doi:10.1007/978-3-319-24574-4-42
[16] Giannakeas N, Tsiplakidou M, Tsipouras M, Manousou P, Forlano R et al. Image Enhancement of Routine Biopsies: A Case for Liver Tissue Detection. 2017 IEEE 17th International Conference on Bioinformatics and Bioengineering (BIBE). 2017 doi:10.1109/bibe.2017.00-49
[17] Pang Y, Sun M, Jiang X, Li X. Convolution in Convolution for Network in Network. IEEE Transactions on Neural Networks and Learning Systems. 2018;29 (5):1587-1597. doi:10.1109/tnnls.2017.2676130
[18] Gavrilov A, Jordache A, Vasdani M, Deng J. Preventing Model Overfitting and Underfitting in Convolutional Neural Networks. International Journal of Software Science and Computational Intelligence. 2018;10 (4):19-28. doi:10.4018/ijssci.2018100102
[19] Dapson R, Horobin R, Kiernan J. Hematoxylin shortages: their causes and duration, and other dyes that can replace hemalum in routine hematoxylin and eosin staining. Biotechnic & Histochemistry. 2010;85 (1):55-63. doi:10.3109/10520290903048400
[20] Gultom Y, Arymurthy A, Masikome R. Batik Classification using Deep Convolutional Network Transfer Learning. Jurnal Ilmu Komputer dan Informasi. 2018;11 (2):59. doi:10.21609/jiki.v11i2.507
[21] Shallu, Mehra R. Breast cancer histology images classification: Training from scratch or transfer learning?. ICT Express. 2018;4 (4):247-254. doi:10.1016/j.icte.2018.10.007
[22] Huang Y, Wu R, Sun Y, Wang W, Ding X. Vehicle Logo Recognition System Based on Convolutional Neural Networks With a Pretraining Strategy. IEEE Transactions on Intelligent Transportation Systems. 2015;16 (4):1951- 1960. doi:10.1109/tits.2014.2387069
[23] Coudray N, Ocampo P, Sakellaropoulos T, Narula N, Snuderl M et al. Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning. Nature Medicine. 2018;24 (10):1559-1567. doi:10.1038/s41591-018-0177-5
[24] Veta M, Pluim J, van Diest P, Viergever M. Breast Cancer Histopathology Image Analysis: A Review. IEEE Transactions on Biomedical Engineering. 2014;61 (5):1400-1411. doi:10.1109/tbme.2014.2303852
[25] Xing F, Su H, Neltner J, Yang L. Automatic Ki-67 Counting Using Robust Cell Detection and Online Dictionary Learning. IEEE Transactions on Biomedical Engineering. 2014;61 (3):859-870. doi:10.1109/tbme.2013.2291703
[26] Jung C, Kim C. Segmenting Clustered Nuclei Using H-minima Transform-Based Marker Extraction and Contour Parameterization. IEEE Transactions on Biomedical Engineering. 2010;57 (10):2600-2604. doi:10.1109/tbme.2010.2060336
[27] Hameed Z, Zahia S, Garcia-Zapirain B, Javier Aguirre J, María Vanegas A. Breast Cancer Histopathology Image Classification Using an Ensemble of Deep Learning Models. Sensors. 2020;20 (16):4373. doi:10.3390/s20164373
[28] Aksac A, Demetrick D, Ozyer T, Alhajj R. BreCaHAD: a dataset for breast cancer histopathological annotation and diagnosis. BMC Research Notes. 2019;12 (1). doi:10.1186/s13104-019-4121-7
[29] Tellez D, Balkenhol M, Otte-Holler I, van de Loo R, Vogels R et al. Whole-Slide Mitosis Detection in H&E Breast Histology Using PHH3 as a Reference to Train Distilled Stain-Invariant Convolutional Networks. IEEE Transactions on Medical Imaging. 2018;37 (9):2126-2136. doi:10.1109/tmi.2018.2820199
[30] Couture H, Williams L, Geradts J, Nyante S, Butler E et al. Image analysis with deep learning to predict breast cancer grade, ER status, histologic subtype, and intrinsic subtype. npj Breast Cancer. 2018;4 (1). doi:10.1038/s41523- 018-0079-1
[31] Gu J, Wang Z, Kuen J, Ma L, Shahroudy A et al. Recent advances in convolutional neural networks. Pattern Recognition. 2018;77:354-377. doi:10.1016/j.patcog.2017.10.013
[32] Breast Density [Internet]. Breastlink. 2021 [cited 17 June 2021]. Available from: https://www.breastlink.com/breastcancer-101/risk-factors/breast-density/
[33] Choi J, Kim H, Kim W, Lim I, Lee I et al. Early prediction of neoadjuvant chemotherapy response for advanced breast cancer using PET/MRI image deep learning. Scientific Reports. 2020;10 (1). doi:10.1038/s41598-020-77875-5
[34] Dodge S, Karam L. Human and DNN Classification Performance on Images With Quality Distortions. ACM Transactions on Applied Perception. 2019;16 (2):1-17. doi:10.1145/3306241
[35] Erickson B, Korfiatis P, Kline T, Akkus Z, Philbrick K. Deep Learning in Radiology: Does One Size Fit All?. Journal of the American College of Radiology. 2018;15 (3):521-526. doi:10.1016/j.jacr.2017.12.027
[36] Hsu S, Yeh L, Chen T, Du W, Huang Y et al. Classification of the Multiple Stages of Parkinson’s Disease by a Deep Convolution Neural Network Based on 99mTc-TRODAT-1 SPECT Images. Molecules. 2020;25 (20):4792. doi:10.3390/molecules25204792
[37] Krizhevsky A, Sutskever I, Hinton G. ImageNet classification with deep convolutional neural networks. Communications of the ACM. 2017;60 (6):84-90. doi:10.1145/3065386
[38] Simonyan K, Zisserman A. Very Deep Convolutional Networks for Large-Scale Image Recognition. 3rd International Conference on Learning Representations (ICLR). 2015.
[39] Shen L, Margolies L, Rothstein J, Fluder E, McBride R et al. Deep Learning to Improve Breast Cancer Detection on Screening Mammography. Scientific Reports. 2019;9 (1). doi:10.1038/s41598-019-48995-4
[40] Zhou X, Li C, Rahaman M, Yao Y, Ai S et al. A Comprehensive Review for Breast Histopathology Image Analysis Using Classical and Deep Neural Networks. IEEE Access. 2020;8:90931-90956. doi:10.1109/access.2020.2993788
[41] Anghel A, Stanisavljevic M, Andani S, Papandreou N, Rüschoff J et al. A High-Performance System for Robust Stain Normalization of Whole-Slide Images in Histopathology. Frontiers in Medicine. 2019;6. doi:10.3389/fmed.2019.00193
[42] Zhu C, Song F, Wang Y, Dong H, Guo Y et al. Breast cancer histopathology image classification through assembling multiple compact CNNs. BMC Medical Informatics and Decision Making. 2019;19 (1). doi:10.1186/s12911-019-0913- x
[43] Shahidi F, Mohd Daud S, Abas H, Ahmad N, Maarop N. Breast Cancer Classification Using Deep Learning Approaches and Histopathology Image: A Comparison Study. IEEE Access. 2020;8:187531-187552. doi:10.1109/access.2020.3029881
[44] Wang S, Lv Y, Sui Y, Liu S, Wang S et al. Alcoholism Detection by Data Augmentation and Convolutional Neural Network with Stochastic Pooling. Journal of Medical Systems. 2017;42 (1). doi:10.1007/s10916-017-0845-x
[45] Perone C, Calabrese E, Cohen-Adad J. Spinal cord gray matter segmentation using deep dilated convolutions. Scientific Reports. 2018;8 (1). doi:10.1038/s41598-018-24304-3
[46] Lin Y, Wu J. A Novel Multichannel Dilated Convolution Neural Network for Human Activity Recognition. Mathematical Problems in Engineering. 2020;2020:1-10.doi:10.1155/2020/5426532
[47] Wang B, Zhang X, Zhou X, Li J. A Gated Dilated Convolution with Attention Model for Clinical Cloze-Style Reading Comprehension. International Journal of Environmental Research and Public Health. 2020;17 (4):1323. doi:10.3390/ijerph17041323
[48] Han Z, Wei B, Zheng Y, Yin Y, Li K et al. Breast Cancer Multi-classification from Histopathological Images with Structured Deep Learning Model. Scientific Reports. 2017;7 (1). doi:10.1038/s41598-017-04075-z
[49] Zhang J, Lin S, Ding L, Bruzzone L. Multi-Scale Context Aggregation for Semantic Segmentation of Remote Sensing Images. Remote Sensing. 2020;12 (4):701. doi:10.3390/rs12040701
[50] Li X, Shen X, Zhou Y, Wang X, Li T. Classification of breast cancer histopathological images using interleaved DenseNet with SENet (IDSNet). PLOS ONE. 2020;15 (5):e0232127. doi:10.1371/journal.pone.0232127