Abstract
Breast cancer is one of the substantial diseases that affect millions of females each year also the velocity of affected individuals is rising every year. Timely recognition of the illness is the only feasible solution to reduce its influence of the disease. Numerous techniques are invented by researchers in support of the determination of breast cancer and the usage of histopathology descriptions provided the auspicious solution. As an enhancement, in this research, a Deer-Canid based deep CNN is implemented by means of the histopathology images used for the detection of breast cancer through the taxonomy of benign, malignant, and normal regions. The segmentation of the histopathology images is performed using the V-net architecture that segments the image without losing its originality. The primary involvement of the research relies on the Deer-Canid optimization that helps in attaining the global best solution and effectively minimizes the time taken for the classification. The superiority of the research is proved by measuring the values of accuracy, precision, recall, and f1 measure, and the proposed Deer Canid optimization-based deep CNN attained the values of 92.967%, 94.342%, 93.454%, 92.896%, which is more efficient.
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References
Ahlawat S, Choudhary A, Nayyar A, Singh S, Yoon B (2020) Improved handwritten digit recognition using convolutional neural networks (CNN). Sensors 20(12):3344
Chen Y, Hu X, Fan W, Shen L, Zhang Z, Liu X, Du J, Li H, Chen Y, Li H (2020) Fast density peak clustering for large scale data based on kNN. Knowl-Based Syst 187:104824
Das K, Conjeti S, Chatterjee J, Sheet D (2020) Detection of breast cancer from whole slide histopathological images using deep multiple instance CNN. IEEE Access 8:213502–213511
Desai M, Shah M (2021) An anatomization on breast cancer detection and diagnosis employing multi-layer perceptron neural network (MLP) and convolutional neural network (CNN). Clin eHealth 4:1–11
Feng Y, Zhang L, Mo J (2020) Deep manifold preserving autoencoder for classifying breast cancer histopathological images. IEEE ACM Trans Comput Biol Bioinform 17(1):91–101
Gregorio GD, Desiato D, Marcelli A, Polese G (2021) A multi classifier approach for supporting Alzheimer’s diagnosis based on handwriting analysis. Int C Patt Recog 559–574
Gurcan MN, Boucheron LE, Can A, Madabhushi A, Rajpoot NM, Yener B (2009) Histopathological image analysis: a review. IEEE Rev Biomed Eng 2:147–171
Hirra I, Ahmad M, Hussain A, Ashraf MU, Saeed IA, Qadri SF, Alghamdi AM, Alfakeeh AS (2021) Breast cancer classification from histopathological images using patch-based deep learning modeling. IEEE Access 9:24273–24287
Li G, Li C, Wu G, Ji D, Zhang H (2021) Multi-view attention-guided multiple instance detection network for interpretable breast cancer histopathological image diagnosis. IEEE Access 9:79671–79684
Maan J, Maan H (2022) Breast cancer detection using histopathological images, in arXiv e-prints arXiv:2202.06109. IJCST 10(1):53–58
Mishra S (2022) Artificial intelligence: a review of progress and prospects in medicine and healthcare. J Electron Electromed Eng Med Inform 4(1):1–23
Ning Z, Zhang X, Tu C, Feng Q, Zhang Y (2019) Multiscale context-cascaded ensemble framework (MsC2EF): application to breast histopathological image. IEEE Access 7:150910–150923
Qi Q, Li Y, Wang J, Zheng H, Huang Y, Ding X, Rohde GK (2019) Label-efficient breast cancer histopathological image classification. IEEE J Biomed Health 23(5):2108–2116
Rubin R, Strayer DS (2008) Rubin's pathology: clinicopathologic foundations of medicine (5th ed.). Wolters Kluwer/Lippincott Williams & Wilkins 12:1341
Sahoo KS, Tripathy BK, Naik K, Ramasubbareddy S, Balusamy B, Khari M, Burgos D (2020) An evolutionary SVM model for DDOS attack detection in software defined networks. IEEE Access 8:132502–132513
Sekaran K, Chandana P, Krishna NM, Kadry S (2020) Deep learning convolutional neural network (CNN) with Gaussian mixture model for predicting pancreatic cancer. Multimed Tools Appl 79(15):10233–10247
Shahraki N, Mohammad H, Taghian S, Mirjalili S (2021) An improved grey wolf optimizer for solving engineering problems. Expert Syst Appl 166:113917
Smith RA, Cokkinides V, Eschenbach AC, Levin B, Cohen C, Runowicz CD, Sener S, Saslow D, Eyre HJ (2002) American cancer society guidelines for the early detection of cancer. CA Cancer J Clin 52(1):8–22
Tian M-W, Yan S-R, Han S-Z, Nojavan S, Jermsittiparsert K, Razmjooy N (2020) New optimal design for a hybrid solar chimney, solid oxide electrolysis and fuel cell based on improved deer hunting optimization algorithm. J Clean Prod 24:119414
Yan R, Ren F, Wang Z, Wang L, Zhang T, Liu Y, Rao X, Zheng C, Zhang F (2020) Breast cancer histopathological image classification using a hybrid deep neural network. Methods 173:52–60
Yann LC, Bengio Y, Hinton G (2015) Deep learning. Nature 521(7553):436–444
Zhang H, Wu R, Yuan T, Jiang Z, Huang S, Wu J, Hua J, Niu Z, Ji D (2020) DE-Ada: a novel model for breast mass classification using cross-modal pathological semantic mining and organic integration of multi-feature fusions. Inf Sci 539:461–486
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Bhausaheb, D.P., Kashyap, K.L. Detection and classification of breast cancer availing deep canid optimization based deep CNN. Multimed Tools Appl 82, 18019–18037 (2023). https://doi.org/10.1007/s11042-022-14268-y
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DOI: https://doi.org/10.1007/s11042-022-14268-y