{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:50:26Z","timestamp":1777704626387,"version":"3.51.4"},"reference-count":10,"publisher":"SAGE Publications","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2020,12,4]]},"abstract":"<jats:p>Due to the wide acceptance of White Blood Cells (WBCs) in disease diagnosis, detection and classification of WBC are hot topic. Existing methodologies have some drawbacks such as significant degree of error, higher accuracy, time bound and higher misclassification rate. A WBCs detection and classification called, Jenks Optimized Logistic Convolutional Neural Network (JO-LCNN) method has proposed. Initally, Eulers Principal Axis is used as a convolution model to obtain a rotation invariant form of image by differentiating the background and RBCs, then eliminating them which leaves only the WBCs. By eliminating the wanton features, inherent features are detected contributing to minimum misclassification rate. According to above, Jenks Optimization function is used as a pooling model to obtain feature map for lower resolution. Therefore JO-LCNN is used for removing tiny objects in image and complete nuclei. Finally, Multinomial Logistic classifier is used to classify five types of classes by means of loss function and updating weight according to the loss function, therefore classifying with higher accuracy rate. Using LISC database for WBCs with different parameters as classification accuracy, false positive rate and time complexity are performed. Result shows that JO-LCNN, efficiently improves accuracy with less time, misclassification rate than the state-of-art methods.<\/jats:p>","DOI":"10.3233\/jifs-189152","type":"journal-article","created":{"date-parts":[[2020,8,11]],"date-time":"2020-08-11T15:03:32Z","timestamp":1597158212000},"page":"8333-8343","source":"Crossref","is-referenced-by-count":2,"title":["White blood cell detection and classification using Euler\u2019s Jenks optimized multinomial logistic neural networks"],"prefix":"10.1177","volume":"39","author":[{"given":"M.P.","family":"Karthikeyan","sequence":"first","affiliation":[{"name":"School of Computing, SASTRA Deemed University, Thanjavur, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"R.","family":"Venkatesan","sequence":"additional","affiliation":[{"name":"School of Computing, SASTRA Deemed University, Thanjavur, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"V.","family":"Vijayakumar","sequence":"additional","affiliation":[{"name":"Adjunct Professor, Noble International University, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Logesh","family":"Ravi","sequence":"additional","affiliation":[{"name":"Sri Ramachandra Faculty of Engineering and Technology, Sri Ramachandra Institute of Higher Education and Research, Chennai, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"V.","family":"Subramaniyaswamy","sequence":"additional","affiliation":[{"name":"School of Computing, SASTRA Deemed University, Thanjavur, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"key":"10.3233\/JIFS-189152_ref3","doi-asserted-by":"crossref","unstructured":"Riccio D. , Brancati N. , Frucci M. and Gragnaniello D. , A New Unsupervised Approach for Segmenting and Counting Cells in High-Throughput Microscopy Image Sets, IEEE Journal of Biomedical and Health Informatics 23(1), Jan 2019.","DOI":"10.1109\/JBHI.2018.2817485"},{"key":"10.3233\/JIFS-189152_ref5","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0133489"},{"key":"10.3233\/JIFS-189152_ref6","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0189259"},{"key":"10.3233\/JIFS-189152_ref8","doi-asserted-by":"crossref","unstructured":"Bills M.V. , Nguyen B.T. and Yoon J.-Y. , Simplified White Blood Cell Differential: An Inexpensive, Smartphone- and Paper-Based Blood Cell Count, IEEE Sensors Journal 19(18) Sep 2019.","DOI":"10.1109\/JSEN.2019.2920235"},{"key":"10.3233\/JIFS-189152_ref9","doi-asserted-by":"crossref","unstructured":"Othman M.Z. , Mohammed T.S. and Ali A.B. , Neural Network Classification of White Blood Cell using Microscopic Images, International Journal of Advanced Computer Science and Applications 8(5), 2017.","DOI":"10.14569\/IJACSA.2017.080513"},{"key":"10.3233\/JIFS-189152_ref10","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pcbi.1005746"},{"key":"10.3233\/JIFS-189152_ref13","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0218808"},{"issue":"4","key":"10.3233\/JIFS-189152_ref14","doi-asserted-by":"crossref","first-page":"333","DOI":"10.1016\/j.compmedimag.2011.01.003","article-title":"Automatic recognition of five types of white blood cells in peripheral blood","volume":"35","author":"Rezatofighi","year":"2011","journal-title":"Computerized Medical Imaging and Graphics"},{"key":"10.3233\/JIFS-189152_ref16","doi-asserted-by":"crossref","unstructured":"Liu X. , Huang X. , Jiang Y. , Xu H. , Guo J. , Hou H.W. , Yan M. and Yu H. , A Microfluidic Cytometer for Complete Blood Count With a 3.2-Megapixel, 1.1- \u03bcm-Pitch Super-Resolution Image Sensor in 65-nm BSI CMOS, IEEE Transactions on Biomedical Circuits and Systems 11(4), Aag 2017.","DOI":"10.1109\/TBCAS.2017.2697451"},{"key":"10.3233\/JIFS-189152_ref20","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0211347"}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"link":[{"URL":"https:\/\/content.iospress.com\/download?id=10.3233\/JIFS-189152","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:41:39Z","timestamp":1777455699000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/full\/10.3233\/JIFS-189152"}},"subtitle":[],"editor":[{"given":"Vijayakumar","family":"Varadarajan","sequence":"additional","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]},{"given":"Piet","family":"Kommers","sequence":"additional","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]},{"given":"Vincenzo","family":"Piuri","sequence":"additional","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]},{"given":"V.","family":"Subramaniyaswamy","sequence":"additional","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2020,12,4]]},"references-count":10,"journal-issue":{"issue":"6"},"URL":"https:\/\/doi.org\/10.3233\/jifs-189152","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,12,4]]}}}