{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T16:34:27Z","timestamp":1780677267110,"version":"3.54.1"},"reference-count":43,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2020,8,22]],"date-time":"2020-08-22T00:00:00Z","timestamp":1598054400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Cancer identification and classification from histopathological images of the breast depends greatly on experts, and computer-aided diagnosis can play an important role in disagreement of experts. This automatic process has increased the accuracy of the classification at a reduced cost. The advancement in Convolution Neural Network (CNN) structure has outperformed the traditional approaches in biomedical imaging applications. One of the limiting factors of CNN is it uses spatial image features only for classification. The spectral features from the transform domain have equivalent importance in the complex image classification algorithm. This paper proposes a new CNN structure to classify the histopathological cancer images based on integrating the spectral features obtained using a multi-resolution wavelet transform with the spatial features of CNN. In addition, batch normalization process is used after every layer in the convolution network to improve the poor convergence problem of CNN and the deep layers of CNN are trained with spectral\u2013spatial features. The proposed structure is tested on malignant histology images of the breast for both binary and multi-class classification of tissue using the BreaKHis Dataset and the Breast Cancer Classification Challenge 2015 Datasest. Experimental results show that the combination of spectral\u2013spatial features improves classification accuracy of the CNN network and requires less training parameters in comparison with the well known models (i.e., VGG16 and ALEXNET). The proposed structure achieves an average accuracy of 97.58% and 97.45% with 7.6 million training parameters on both datasets, respectively.<\/jats:p>","DOI":"10.3390\/s20174747","type":"journal-article","created":{"date-parts":[[2020,8,23]],"date-time":"2020-08-23T21:28:06Z","timestamp":1598218086000},"page":"4747","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":47,"title":["Spectral\u2013Spatial Features Integrated Convolution Neural Network for Breast Cancer Classification"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3579-8708","authenticated-orcid":false,"given":"Hiren K","family":"Mewada","sequence":"first","affiliation":[{"name":"Electrical Engineering Department, Prince Mohammad Bin Fahd University, Al Khobar 31952, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5358-843X","authenticated-orcid":false,"given":"Amit V","family":"Patel","sequence":"additional","affiliation":[{"name":"CHARUSAT Space Research and Technology Center, Charotar University of Science and Technology, Changa, Gujarat 388421, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5655-8511","authenticated-orcid":false,"given":"Mahmoud","family":"Hassaballah","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Faculty of Computers and Information, South Valley University, Qena 83523, Egypt"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7658-7085","authenticated-orcid":false,"given":"Monagi H.","family":"Alkinani","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Artificial Intelligence, College of Computer Science and Engineering, University of Jeddah, Jeddah 21959, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0441-6057","authenticated-orcid":false,"given":"Keyur","family":"Mahant","sequence":"additional","affiliation":[{"name":"CHARUSAT Space Research and Technology Center, Charotar University of Science and Technology, Changa, Gujarat 388421, India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2020,8,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Silva, T.A.E.D., Silva, L.F.D., Muchaluat-Saade, D.C., and Conci, A. (2020). A Computational Method to Assist the Diagnosis of Breast Disease Using Dynamic Thermography. Sensors, 20.","DOI":"10.3390\/s20143866"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Aldhaeebi, M.A., Alzoubi, K., Almoneef, T.S., Bamatraf, S.M., Attia, H., and Ramahi, O.M. (2020). Review of Microwaves Techniques for Breast Cancer Detection. Sensors, 20.","DOI":"10.3390\/s20082390"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"90931","DOI":"10.1109\/ACCESS.2020.2993788","article-title":"A Comprehensive Review for Breast Histopathology Image Analysis Using Classical and Deep Neural Networks","volume":"8","author":"Zhou","year":"2020","journal-title":"IEEE Access"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"4022","DOI":"10.3390\/s20144022","article-title":"Electrochemical Nanobiosensors for Detection of Breast Cancer Biomarkers","volume":"20","author":"Veronika","year":"2020","journal-title":"Sensors"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Abrao Nemeir, I., Saab, J., Hleihel, W., Errachid, A., Jafferzic-Renault, N., and Zine, N. (2019). The advent of salivary breast cancer biomarker detection using affinity sensors. Sensors, 19.","DOI":"10.3390\/s19102373"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Mambou, S.J., Maresova, P., Krejcar, O., Selamat, A., and Kuca, K. (2018). Breast cancer detection using infrared thermal imaging and a deep learning model. Sensors, 18.","DOI":"10.3390\/s18092799"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1016\/j.eswa.2016.01.014","article-title":"Unsupervised event detection and classification of multichannel signals","volume":"54","author":"Mur","year":"2016","journal-title":"Expert Syst. Appl."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Hassaballah, M., and Awad, A.I. (2016). Detection and description of image features: An introduction. Image Feature Detectors and Descriptors, Springer.","DOI":"10.1007\/978-3-319-28854-3_1"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Awad, A.I., and Hassaballah, M. (2016). Image Feature Detectors and Descriptors: Foundations and Applications, Springer.","DOI":"10.1007\/978-3-319-28854-3"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Hassaballah, M., and Hosny, K.M. (2018). Recent Advances in Computer Vision: Theories and Applications, Springer.","DOI":"10.1007\/978-3-030-03000-1"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Hassaballah, M., and Awad, A.I. (2020). Deep Learning in Computer Vision: Principles and Applications, CRC Press.","DOI":"10.1201\/9781351003827"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Gour, M., Jain, S., and Sunil Kumar, T. (2020). Residual learning based CNN for breast cancer histopathological image classification. Int. J. Imaging Syst. Technol.","DOI":"10.1002\/ima.22403"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"605","DOI":"10.1007\/s10278-019-00182-7","article-title":"Breast cancer classification from histopathological images with inception recurrent residual convolutional neural network","volume":"32","author":"Alom","year":"2019","journal-title":"J. Digit. Imaging"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"1455","DOI":"10.1109\/TBME.2015.2496264","article-title":"A dataset for breast cancer histopathological image classification","volume":"63","author":"Spanhol","year":"2015","journal-title":"IEEE Trans. Biomed. Eng."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2018\/2362108","article-title":"Histopathological breast cancer image classification by deep neural network techniques guided by local clustering","volume":"2018","author":"Nahid","year":"2018","journal-title":"BioMed Res. Int."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"80","DOI":"10.3389\/fgene.2019.00080","article-title":"Deep learning based analysis of histopathological images of breast cancer","volume":"10","author":"Xie","year":"2019","journal-title":"Front. Genet."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Jiang, Y., Chen, L., Zhang, H., and Xiao, X. (2019). Breast cancer histopathological image classification using convolutional neural networks with small SE-ResNet module. PLoS ONE, 14.","DOI":"10.1371\/journal.pone.0214587"},{"key":"ref_18","unstructured":"Wei, B., Han, Z., He, X., and Yin, Y. (2017, January 28\u201330). Deep learning model based breast cancer histopathological image classification. Proceedings of the International Conference on Cloud Computing and Big Data Analysis, Chengdu, China."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2068","DOI":"10.4066\/biomedicalresearch.29-17-3903","article-title":"Histopathological breast-image classification with restricted Boltzmann machine along with backpropagation","volume":"29","author":"Nahid","year":"2018","journal-title":"Biomed. Res."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Mahbod, A., Ellinger, I., Ecker, R., Smedby, \u00d6., and Wang, C. (2018). Breast cancer histological image classification using fine-tuned deep network fusion. International Conference on Image Analysis and Recognition, Springer.","DOI":"10.1007\/978-3-319-93000-8_85"},{"key":"ref_21","first-page":"1","article-title":"Breast cancer multi-classification from histopathological images with structured deep learning model","volume":"7","author":"Han","year":"2017","journal-title":"Sci. Rep."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Spanhol, F.A., Oliveira, L.S., Petitjean, C., and Heutte, L. (2016). Breast cancer histopathological image classification using convolutional neural networks. International Joint Conference on Neural Networks, IEEE.","DOI":"10.1109\/IJCNN.2016.7727519"},{"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","first-page":"316","article-title":"Multi-class breast cancer classification using deep learning convolutional neural network","volume":"9","author":"Nawaz","year":"2018","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.neucom.2019.09.044","article-title":"BreakHis based breast cancer automatic diagnosis using deep learning: Taxonomy, survey and insights","volume":"375","author":"Benhammou","year":"2020","journal-title":"Neurocomputing"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"405","DOI":"10.1016\/j.ins.2019.08.072","article-title":"Deep feature learning for histopathological image classification of canine mammary tumors and human breast cancer","volume":"508","author":"Kumar","year":"2020","journal-title":"Inf. Sci."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"14509","DOI":"10.1007\/s11042-018-6970-9","article-title":"Multi-task deep learning for fine-grained classification and grading in breast cancer histopathological images","volume":"79","author":"Li","year":"2020","journal-title":"Multimed. Tools Appl."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"632","DOI":"10.1007\/s10278-019-00307-y","article-title":"Conventional Machine Learning and Deep Learning Approach for Multi-Classification of Breast Cancer Histopathology Images\u2014A Comparative Insight","volume":"33","author":"Sharma","year":"2020","journal-title":"J. Digit. Imaging"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Ara\u00fajo, T., Aresta, G., Castro, E., Rouco, J., Aguiar, P., Eloy, C., Pol\u00f3nia, A., and Campilho, A. (2017). Classification of breast cancer histology images using convolutional neural networks. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0177544"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Zhu, C., Song, F., Wang, Y., Dong, H., Guo, Y., and Liu, J. (2019). Breast cancer histopathology image classification through assembling multiple compact CNNs. BMC Med. Informatics Decis. Mak., 19.","DOI":"10.1186\/s12911-019-0913-x"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Das, K., Conjeti, S., Roy, A.G., Chatterjee, J., and Sheet, D. (2018, January 4\u20137). Multiple instance learning of deep convolutional neural networks for breast histopathology whole slide classification. Proceedings of the IEEE International Symposium on Biomedical Imaging, Washington, DC, USA.","DOI":"10.1109\/ISBI.2018.8363642"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2828","DOI":"10.1016\/j.sigpro.2012.06.029","article-title":"Analysis of nuclei textures of fine needle aspirated cytology images for breast cancer diagnosis using complex Daubechies wavelets","volume":"93","author":"Niwas","year":"2013","journal-title":"Signal Process."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"967","DOI":"10.1016\/j.bbe.2019.09.003","article-title":"HWDCNN: Multi-class recognition in breast histopathology with Haar wavelet decomposed image based convolution neural network","volume":"39","author":"Kausar","year":"2019","journal-title":"Biocybern. Biomed. Eng."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. (2017, January 21\u201326). Densely connected convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"211","DOI":"10.1007\/s11263-015-0816-y","article-title":"Imagenet large scale visual recognition challenge","volume":"115","author":"Russakovsky","year":"2015","journal-title":"Int. J. Comput. Vis."},{"key":"ref_37","unstructured":"Liu, Y., Gadepalli, K., Norouzi, M., Dahl, G.E., Kohlberger, T., Boyko, A., Venugopalan, S., Timofeev, A., Nelson, P.Q., and Corrado, G.S. (2017). Detecting cancer metastases on gigapixel pathology images. arXiv."},{"key":"ref_38","unstructured":"Pego, A., and Aguiar, P. (2020, July 21). Bioimaging 2015. Available online: http:\/\/www.bioimaging2015.ineb.up.pt\/."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Cire\u015fan, D.C., Giusti, A., Gambardella, L.M., and Schmidhuber, J. (2013). Mitosis detection in breast cancer histology images with deep neural networks. International Conference on Medical Image Computing and Computer-Assisted Intervention, Springer.","DOI":"10.1007\/978-3-642-40763-5_51"},{"key":"ref_40","first-page":"4688","article-title":"Computerized nuclear morphometry as an objective method for characterizing human cancer cell populations","volume":"38","author":"Stenkvist","year":"1978","journal-title":"Cancer Res."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1563","DOI":"10.1016\/j.compbiomed.2013.08.003","article-title":"Computer-aided diagnosis of breast cancer based on fine needle biopsy microscopic images","volume":"43","author":"Kowal","year":"2013","journal-title":"Comput. Biol. Med."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"2169","DOI":"10.1109\/TMI.2013.2275151","article-title":"Computer-aided breast cancer diagnosis based on the analysis of cytological images of fine needle biopsies","volume":"32","author":"Filipczuk","year":"2013","journal-title":"IEEE Trans. Med Imaging"},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"949","DOI":"10.1109\/JSYST.2013.2279415","article-title":"Remote computer-aided breast cancer detection and diagnosis system based on cytological images","volume":"8","author":"George","year":"2013","journal-title":"IEEE Syst. J."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/17\/4747\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:05:11Z","timestamp":1760177111000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/17\/4747"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,8,22]]},"references-count":43,"journal-issue":{"issue":"17","published-online":{"date-parts":[[2020,9]]}},"alternative-id":["s20174747"],"URL":"https:\/\/doi.org\/10.3390\/s20174747","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,8,22]]}}}