{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,19]],"date-time":"2026-09-19T17:22:32Z","timestamp":1789838552054,"version":"4.0.1"},"reference-count":66,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2022,2,21]],"date-time":"2022-02-21T00:00:00Z","timestamp":1645401600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Breast cancer has affected many women worldwide. To perform detection and classification of breast cancer many computer-aided diagnosis (CAD) systems have been established because the inspection of the mammogram images by the radiologist is a difficult and time taken task. To early diagnose the disease and provide better treatment lot of CAD systems were established. There is still a need to improve existing CAD systems by incorporating new methods and technologies in order to provide more precise results. This paper aims to investigate ways to prevent the disease as well as to provide new methods of classification in order to reduce the risk of breast cancer in women\u2019s lives. The best feature optimization is performed to classify the results accurately. The CAD system\u2019s accuracy improved by reducing the false-positive rates.The Modified Entropy Whale Optimization Algorithm (MEWOA) is proposed based on fusion for deep feature extraction and perform the classification. In the proposed method, the fine-tuned MobilenetV2 and Nasnet Mobile are applied for simulation. The features are extracted, and optimization is performed. The optimized features are fused and optimized by using MEWOA. Finally, by using the optimized deep features, the machine learning classifiers are applied to classify the breast cancer images. To extract the features and perform the classification, three publicly available datasets are used: INbreast, MIAS, and CBIS-DDSM. The maximum accuracy achieved in INbreast dataset is 99.7%, MIAS dataset has 99.8% and CBIS-DDSM has 93.8%. Finally, a comparison with other existing methods is performed, demonstrating that the proposed algorithm outperforms the other approaches.<\/jats:p>","DOI":"10.3390\/diagnostics12020557","type":"journal-article","created":{"date-parts":[[2022,2,21]],"date-time":"2022-02-21T20:24:21Z","timestamp":1645475061000},"page":"557","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":104,"title":["Breast Cancer Mammograms Classification Using Deep Neural Network and Entropy-Controlled Whale Optimization Algorithm"],"prefix":"10.3390","volume":"12","author":[{"given":"Saliha","family":"Zahoor","sequence":"first","affiliation":[{"name":"Computer Science Department, University of Gujrat, Gujrat 50700, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Umar","family":"Shoaib","sequence":"additional","affiliation":[{"name":"Computer Science Department, University of Gujrat, Gujrat 50700, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ikram Ullah","family":"Lali","sequence":"additional","affiliation":[{"name":"Information Sciences Department, University of Education Lahore, Jauhrabad Campus, Khushab 41200, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"209","DOI":"10.3322\/caac.21660","article-title":"Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries","volume":"71","author":"Sung","year":"2021","journal-title":"CA Cancer J. Clin."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"3212","DOI":"10.1109\/TNNLS.2018.2876865","article-title":"Object detection with deep learning: A review","volume":"30","author":"Zhao","year":"2019","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"109","DOI":"10.4103\/2231-4040.72251","article-title":"Various types and management of breast cancer: An overview","volume":"1","author":"Sharma","year":"2010","journal-title":"J. Adv. Pharm. Technol. Res."},{"key":"ref_4","first-page":"291","article-title":"Errors in mammography cannot be solved through technology alone","volume":"19","author":"Ekpo","year":"2018","journal-title":"Asian Pac. J. Cancer Prev. APJCP"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"2353","DOI":"10.1056\/NEJMsr1504363","article-title":"Breast-cancer screening\u2014Viewpoint of the IARC Working Group","volume":"372","author":"Scoccianti","year":"2015","journal-title":"N. Engl. J. Med."},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Khan, M.A., Ashraf, I., Alhaisoni, M., Dama\u0161evi\u010dius, R., Scherer, R., Rehman, A., and Bukhari, S.A.C. (2020). Multimodal brain tumor classification using deep learning and robust feature selection: A machine learning application for radiologists. Diagnostics, 10.","DOI":"10.3390\/diagnostics10080565"},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Khan, M.A., Sharif, M., Akram, T., Dama\u0161evi\u010dius, R., and Maskeli\u016bnas, R. (2021). Skin lesion segmentation and multiclass classification using deep learning features and improved moth flame optimization. Diagnostics, 11.","DOI":"10.3390\/diagnostics11050811"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1187","DOI":"10.2174\/1573405616666200406110547","article-title":"Breast cancer detection and classification using traditional computer vision techniques: A comprehensive review","volume":"16","author":"Zahoor","year":"2020","journal-title":"Curr. Med. Imaging"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"503","DOI":"10.1038\/s41416-018-0195-6","article-title":"Impact of the second reader on screening outcome at blinded double reading of digital screening mammograms","volume":"119","author":"Coolen","year":"2018","journal-title":"Br. J. Cancer"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1477-7819-12-168","article-title":"Who could benefit the most from using a computer-aided detection system in full-field digital mammography?","volume":"12","author":"Jung","year":"2014","journal-title":"World J. Surg. Oncol."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"781","DOI":"10.1148\/radiol.2203001282","article-title":"Screening mammography with computer-aided detection: Prospective study of 12,860 patients in a community breast center","volume":"220","author":"Freer","year":"2001","journal-title":"Radiology"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"554","DOI":"10.1148\/radiology.215.2.r00ma15554","article-title":"Potential contribution of computer-aided detection to the sensitivity of screening mammography","volume":"215","author":"Wood","year":"2000","journal-title":"Radiology"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Saleem, F., Khan, M.A., Alhaisoni, M., Tariq, U., Armghan, A., Alenezi, F., Choi, J.-I., and Kadry, S. (2021). Human gait recognition: A single stream optimal deep learning features fusion. Sensors, 21.","DOI":"10.3390\/s21227584"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Khan, S., Khan, M.A., Alhaisoni, M., Tariq, U., Yong, H.-S., Armghan, A., and Allenzi, F. (2021). Human Action Recognition: Paradigm of Best Deep Learning Features Selection and Serial Based Extended Fusion. Sensors, 21.","DOI":"10.3390\/s21237941"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Khan, M.A., Alhaisoni, M., Tariq, U., Hussain, N., Majid, A., Dama\u0161evi\u010dius, R., and Maskeliunas, R. (2021). COVID-19 Case Recognition from Chest CT Images by Deep Learning, Entropy-Controlled Firefly Optimization, and Parallel Feature Fusion. Sensors, 21.","DOI":"10.3390\/s21217286"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Azhar, I., Sharif, M., Raza, M., Khan, M.A., and Yong, H.-S. (2021). A Decision Support System for Face Sketch Synthesis Using Deep Learning and Artificial Intelligence. Sensors, 21.","DOI":"10.3390\/s21248178"},{"key":"ref_17","first-page":"100134","article-title":"Deep learning in computer vision: A critical review of emerging techniques and application scenarios","volume":"6","author":"Chai","year":"2021","journal-title":"Mach. Learn. Appl."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Khan, M.A., Rajinikanth, V., Satapathy, S.C., Taniar, D., Mohanty, J.R., Tariq, U., and Damasevicius, R. (2021). VGG19 Network Assisted Joint Segmentation and Classification of Lung Nodules in CT Images. Diagnostics, 11.","DOI":"10.3390\/diagnostics11122208"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Nawaz, M., Nazir, T., Masood, M., Mehmood, A., Mahum, R., Khan, M.A., Kadry, S., and Thinnukool, O. (2021). Analysis of brain MRI images using improved cornernet approach. Diagnostics, 11.","DOI":"10.3390\/diagnostics11101856"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"e6201","DOI":"10.7717\/peerj.6201","article-title":"Breast cancer detection using deep convolutional neural networks and support vector machines","volume":"7","author":"Ragab","year":"2019","journal-title":"PeerJ"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Suzuki, S., Zhang, X., Homma, N., Ichiji, K., Sugita, N., Kawasumi, Y., Ishibashi, T., and Yoshizawa, M. (2016, January 20\u201323). Mass detection using deep convolutional neural network for mammographic computer-aided diagnosis. Proceedings of the 2016 55th Annual Conference of the Society of Instrument and Control Engineers of Japan (SICE), Tsukuba, Japan.","DOI":"10.1109\/SICE.2016.7749265"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"77","DOI":"10.1007\/s10278-014-9719-7","article-title":"Computer-aided diagnosis of malignant mammograms using Zernike moments and SVM","volume":"28","author":"Sharma","year":"2015","journal-title":"J. Digit. Imaging"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-019-48995-4","article-title":"Deep learning to improve breast cancer detection on screening mammography","volume":"9","author":"Shen","year":"2019","journal-title":"Sci. Rep."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"154","DOI":"10.25046\/aj050220","article-title":"Transfer learning and fine tuning in breast mammogram abnormalities classification on CBIS-DDSM database","volume":"5","author":"Falconi","year":"2020","journal-title":"Adv. Sci. Technol. Eng. Syst."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"165724","DOI":"10.1109\/ACCESS.2019.2953318","article-title":"Multi-view feature fusion based four views model for mammogram classification using convolutional neural network","volume":"7","author":"Khan","year":"2019","journal-title":"IEEE Access"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Ansar, W., Shahid, A.R., Raza, B., and Dar, A.H. (2020, January 18\u201319). Breast cancer detection and localization using mobilenet based transfer learning for mammograms. Proceedings of the International Symposium on Intelligent Computing Systems, Sharjah, United Arab Emirates.","DOI":"10.1007\/978-3-030-43364-2_2"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1016\/j.eswa.2018.01.010","article-title":"Computer-aided detection and diagnosis of mammographic masses using multi-resolution analysis of oriented tissue patterns","volume":"99","author":"Chakraborty","year":"2018","journal-title":"Expert Syst. Appl."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"9493","DOI":"10.1007\/s11042-020-09991-3","article-title":"Automatic computer-aided diagnosis system for mass detection and classification in mammography","volume":"80","author":"Lbachir","year":"2021","journal-title":"Multimed. Tools Appl."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"10191","DOI":"10.1007\/s11042-016-3605-x","article-title":"A new fuzzy Gaussian mixture model (FGMM) based algorithm for mammography tumor image classification","volume":"76","author":"Aminikhanghahi","year":"2017","journal-title":"Multimed. Tools Appl."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1016\/j.ijmedinf.2018.06.003","article-title":"A fully integrated computer-aided diagnosis system for digital X-ray mammograms via deep learning detection, segmentation, and classification","volume":"117","author":"Choi","year":"2018","journal-title":"Int. J. Med. Inform."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"747","DOI":"10.1007\/s11045-020-00756-7","article-title":"Diagnosis of breast cancer based on modern mammography using hybrid transfer learning","volume":"32","author":"Khamparia","year":"2021","journal-title":"Multidimens. Syst. Signal Process."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"8860011","DOI":"10.1155\/2020\/8860011","article-title":"A novel algorithm for breast mass classification in digital mammography based on feature fusion","volume":"2020","author":"Zhang","year":"2020","journal-title":"J. Healthc. Eng."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1199","DOI":"10.1007\/s11517-020-02150-8","article-title":"Deep feature-based automatic classification of mammograms","volume":"58","author":"Ridhi","year":"2020","journal-title":"Med. Biol. Eng. Comput."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"114","DOI":"10.1016\/j.media.2017.01.009","article-title":"A deep learning approach for the analysis of masses in mammograms with minimal user intervention","volume":"37","author":"Dhungel","year":"2017","journal-title":"Med. Image Anal."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Garcia-Garcia, A., Orts-Escolano, S., Oprea, S., Villena-Martinez, V., and Garcia-Rodriguez, J. (2017). A review on deep learning techniques applied to semantic segmentation. arXiv.","DOI":"10.1016\/j.asoc.2018.05.018"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"e14464","DOI":"10.2196\/14464","article-title":"Breast cancer detection and diagnosis using mammographic data: Systematic review","volume":"21","author":"Gardezi","year":"2019","journal-title":"J. Med. Internet Res."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"74901","DOI":"10.1109\/ACCESS.2020.2989273","article-title":"Detrac: Transfer learning of class decomposed medical images in convolutional neural networks","volume":"8","author":"Abbas","year":"2020","journal-title":"IEEE Access"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"105584","DOI":"10.1016\/j.cmpb.2020.105584","article-title":"Evaluation of deep learning detection and classification towards a computer-aided diagnosis of breast lesions in digital X-ray mammograms","volume":"196","author":"Han","year":"2020","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-018-22437-z","article-title":"Detecting and classifying lesions in mammograms with deep learning","volume":"8","author":"Ribli","year":"2018","journal-title":"Sci. Rep."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s10916-019-1494-z","article-title":"Classification of mammogram images using multiscale all convolutional neural network (MA-CNN)","volume":"44","author":"Agnes","year":"2020","journal-title":"J. Med. Syst."},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Dhungel, N., Carneiro, G., and Bradley, A.P. (2016, January 17\u201321). The automated learning of deep features for breast mass classification from mammograms. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Athens, Greece.","DOI":"10.1007\/978-3-319-46723-8_13"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"109136","DOI":"10.1016\/j.jcp.2019.109136","article-title":"Adaptive activation functions accelerate convergence in deep and physics-informed neural networks","volume":"404","author":"Jagtap","year":"2020","journal-title":"J. Comput. Phys."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"20200334","DOI":"10.1098\/rspa.2020.0334","article-title":"Locally adaptive activation functions with slope recovery for deep and physics-informed neural networks","volume":"476","author":"Jagtap","year":"2020","journal-title":"Proc. R. Soc. A"},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Jagtap, A.D., Shin, Y., Kawaguchi, K., and Karniadakis, G.E. (2021). Deep Kronecker neural networks: A general framework for neural networks with adaptive activation functions. arXiv.","DOI":"10.1016\/j.neucom.2021.10.036"},{"key":"ref_45","first-page":"2016","article-title":"Curated breast imaging subset of DDSM","volume":"8","author":"Lee","year":"2016","journal-title":"Cancer Imaging Arch."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1016\/j.acra.2011.09.014","article-title":"Inbreast: Toward a full-field digital mammographic database","volume":"19","author":"Moreira","year":"2012","journal-title":"Acad. Radiol."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"105015","DOI":"10.1088\/1361-6560\/abfa35","article-title":"Mass segmentation for whole mammograms via attentive multi-task learning framework","volume":"66","author":"Hou","year":"2021","journal-title":"Phys. Med. Biol."},{"key":"ref_48","doi-asserted-by":"crossref","unstructured":"Attique Khan, M., Sharif, M., Akram, T., Kadry, S., and Hsu, C.H. (2021). A two-stream deep neural network-based intelligent system for complex skin cancer types classification. Int. J. Intell. Syst., 1\u201329.","DOI":"10.1002\/int.22691"},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Sandler, M., Howard, A., Zhu, M., Zhmoginov, A., and Chen, L.-C. (2018, January 18\u201323). Mobilenetv2: Inverted residuals and linear bottlenecks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00474"},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Liu, W., Anguelov, D., Erhan, D., Szegedy, C., Reed, S., Fu, C.-Y., and Berg, A.C. (2016). Ssd: Single shot multibox detector. European Conference on Computer Vision, Springer.","DOI":"10.1007\/978-3-319-46448-0_2"},{"key":"ref_51","doi-asserted-by":"crossref","unstructured":"Zoph, B., Vasudevan, V., Shlens, J., and Le, Q.V. (2018, January 18\u201323). Learning transferable architectures for scalable image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00907"},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Zhang, X., Zhou, X., Lin, M., and Sun, J. (2018, January 18\u201323). Shufflenet: An extremely efficient convolutional neural network for mobile devices. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Salt Lake City, UT, USA.","DOI":"10.1109\/CVPR.2018.00716"},{"key":"ref_53","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Ioffe, S., Vanhoucke, V., and Alemi, A.A. (2017, January 4\u20139). Inception-v4, inception-resnet and the impact of residual connections on learning. Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence, San Francisco, CA, USA.","DOI":"10.1609\/aaai.v31i1.11231"},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201312). Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_55","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2016, January 27\u201330). Rethinking the inception architecture for computer vision. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1186\/s40537-016-0043-6","article-title":"A survey of transfer learning","volume":"3","author":"Weiss","year":"2016","journal-title":"J. Big Data"},{"key":"ref_57","doi-asserted-by":"crossref","first-page":"1345","DOI":"10.1109\/TKDE.2009.191","article-title":"A survey on transfer learning","volume":"22","author":"Pan","year":"2010","journal-title":"IEEE Trans. Knowl. Data Eng."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"105925","DOI":"10.1016\/j.asoc.2019.105925","article-title":"An improved whale optimization algorithm for forecasting water resources demand","volume":"86","author":"Guo","year":"2020","journal-title":"Appl. Soft Comput."},{"key":"ref_59","doi-asserted-by":"crossref","first-page":"102954","DOI":"10.1016\/j.bspc.2021.102954","article-title":"Malignant and nonmalignant classification of breast lesions in mammograms using convolutional neural networks","volume":"70","author":"Yassin","year":"2021","journal-title":"Biomed. Signal Process. Control"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1016\/j.ins.2020.05.080","article-title":"DE-Ada*: A novel model for breast mass classification using cross-modal pathological semantic mining and organic integration of multi-feature fusions","volume":"539","author":"Zhang","year":"2020","journal-title":"Inf. Sci."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Tsochatzidis, L., Costaridou, L., and Pratikakis, I. (2019). Deep learning for breast cancer diagnosis from mammograms\u2014A comparative study. J. Imaging, 5.","DOI":"10.3390\/jimaging5030037"},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Shams, S., Platania, R., Zhang, J., Kim, J., Lee, K., and Park, S.J. (2018, January 16\u201320). Deep generative breast cancer screening and diagnosis. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention, Granada, Spain.","DOI":"10.1007\/978-3-030-00934-2_95"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1016\/j.irbm.2020.12.004","article-title":"Automatic Detection and Classification of Mammograms Using Improved Extreme Learning Machine with Deep Learning","volume":"43","author":"Chakravarthy","year":"2021","journal-title":"IRBM"},{"key":"ref_64","doi-asserted-by":"crossref","first-page":"30735","DOI":"10.1007\/s11042-020-09518-w","article-title":"Breast cancer masses classification using deep convolutional neural networks and transfer learning","volume":"79","author":"Sayed","year":"2020","journal-title":"Multimed. Tools Appl."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"100151","DOI":"10.1016\/j.imu.2019.01.001","article-title":"Intellectual detection and validation of automated mammogram breast cancer images by multi-class SVM using deep learning classification","volume":"16","author":"Kaur","year":"2019","journal-title":"Inform. Med. Unlocked"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1016\/j.eswa.2018.11.008","article-title":"Convolutional neural network improvement for breast cancer classification","volume":"120","author":"Ting","year":"2019","journal-title":"Expert Syst. 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