{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T07:06:05Z","timestamp":1777705565340,"version":"3.51.4"},"reference-count":30,"publisher":"SAGE Publications","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2022,1,25]]},"abstract":"<jats:p>The white corpuscles nucleus segmentation from microscopic blood images is major steps to diagnose blood-related diseases. The perfect and speedy segmentation system assists the hematologists to identify the diseases and take appropriate decision for better treatment. Therefore, fully automated white corpuscles nucleus segmentation model using deep convolution neural network, is proposed in the present study. The proposed model uses the combination of \u2018binary_cross_entropy\u2019 and \u2018adam\u2019 for maintaining learning rate in each network weight. To validate the potential and capability of the above proposed solution, ALL-IDB2 dataset is used. The complete set of images is partitioned into training and testing set and tedious experimentations have been performed. The best performing model is selected and the obtained training and testing accuracy of best performing model is reported as 98.69 % and 99.02 %, respectively. The staging analysis of proposed model is evaluated using sensitivity, specificity, Jaccard index, dice coefficient, accuracy and structure similarity index. The capability of proposed model is compared with performance of the region-based contour and fuzzy-based level-set method for same set of images and concluded that proposed model method is more accurate and effective for clinical purpose.<\/jats:p>","DOI":"10.3233\/jifs-189773","type":"journal-article","created":{"date-parts":[[2021,6,26]],"date-time":"2021-06-26T05:20:46Z","timestamp":1624684846000},"page":"1075-1088","source":"Crossref","is-referenced-by-count":34,"title":["Automated white corpuscles nucleus segmentation using deep neural network from microscopic blood smear"],"prefix":"10.1177","volume":"42","author":[{"given":"Indrajeet","family":"Kumar","sequence":"first","affiliation":[{"name":"Graphic Era Hill University, CSE Department, Dehradun, India"}]},{"given":"Chandradeep","family":"Bhatt","sequence":"additional","affiliation":[{"name":"Graphic Era Hill University, CSE Department, Dehradun, India"}]},{"given":"Vrince","family":"Vimal","sequence":"additional","affiliation":[{"name":"Graphic Era Hill University, CSE Department, Dehradun, India"}]},{"given":"Shamimul","family":"Qamar","sequence":"additional","affiliation":[{"name":"College of Science and Arts Dhahran Al Janub King Khalid University ABHA, Saudi Arabia"}]}],"member":"179","reference":[{"key":"10.3233\/JIFS-189773_ref2","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1109\/RBME.2016.2515127","article-title":"Robust nucleus\/cell detection and segmentation in digital pathology and microscopy images: a comprehensive review","volume":"9","author":"Xing","year":"2016","journal-title":"IEEE Reviews in Biomedical Engineering"},{"key":"10.3233\/JIFS-189773_ref3","doi-asserted-by":"crossref","first-page":"36","DOI":"10.1016\/j.neunet.2018.02.002","article-title":"Inter-class sparsity based discriminative least square regression","volume":"102","author":"Wen","year":"2018","journal-title":"Neural Networks"},{"key":"10.3233\/JIFS-189773_ref4","doi-asserted-by":"crossref","first-page":"315","DOI":"10.1146\/annurev.bioeng.2.1.315","article-title":"Current methods in medical image segmentation","volume":"2","author":"Pham","year":"2000","journal-title":"Annual Review of Biomedical Engineering"},{"key":"10.3233\/JIFS-189773_ref6","doi-asserted-by":"crossref","first-page":"183","DOI":"10.4103\/2228-7477.186885","article-title":"Automatic recognition of acute myelogenous leukemia in blood microscopic images using k-means clustering and support vector machine","volume":"6","author":"Kazemi","year":"2016","journal-title":"Journal of Medical Signals and Sensors"},{"key":"10.3233\/JIFS-189773_ref7","doi-asserted-by":"crossref","first-page":"357","DOI":"10.1007\/s11760-012-0298-0","article-title":"Intracranial hemorrhage detection using spatial fuzzy c-mean and region-based active contour on brain CT imaging","volume":"8","author":"Bhadauria","year":"2014","journal-title":"Signal, Image and Video Processing"},{"key":"10.3233\/JIFS-189773_ref8","doi-asserted-by":"crossref","first-page":"168","DOI":"10.1309\/AJCP78IFSTOGZZJN","article-title":"Automatic recognition of atypical lymphoid cells from peripheral blood by digital image analysis","volume":"143","author":"Alferez","year":"2015","journal-title":"American Journal of Clinical Pathology"},{"key":"10.3233\/JIFS-189773_ref9","doi-asserted-by":"crossref","first-page":"908","DOI":"10.1002\/jemt.22718","article-title":"Computer aided detection and classification of acute lymphoblastic leukemia cell subtypes based on microscopic image analysis","volume":"79","author":"MoradiAmin","year":"2016","journal-title":"Microscopy Research and Technique"},{"key":"10.3233\/JIFS-189773_ref10","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.measurement.2014.04.008","article-title":"Automatic segmentation, counting, size determination and classification of white blood cells","volume":"55","author":"Nazlibilek","year":"2014","journal-title":"Measurement"},{"key":"10.3233\/JIFS-189773_ref12","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1016\/j.bspc.2018.05.010","article-title":"A novel algorithm for segmentation of leukocytes in peripheral blood","volume":"45","author":"Cao","year":"2018","journal-title":"Biomed. 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