{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T12:19:12Z","timestamp":1780661952000,"version":"3.54.1"},"reference-count":29,"publisher":"Walter de Gruyter GmbH","issue":"2","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,4,25]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:sec id=\"j_bmt-2022-0297_abs_001\">\n                  <jats:title>Objectives<\/jats:title>\n                  <jats:p>The leukocyte is a specialized immune cell that functions as the foundation of the immune system and keeps the body healthy. The WBC classification plays a vital role in diagnosing various disorders in the medical area, including infectious diseases, immune deficiencies, leukemia, and COVID-19. A few decades ago, Machine Learning algorithms classified WBC types required for image segmentation, and the feature extraction stages, but this new approach becomes automatic while existing models can be fine-tuned for specific classifications.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec id=\"j_bmt-2022-0297_abs_002\">\n                  <jats:title>Methods<\/jats:title>\n                  <jats:p>The inception architecture and deep learning model-based Resnet connection are integrated into this article. Our proposed method, inception Resnet-v3, was used to classify WBCs into five categories using 15.7k images. Pathologists made diagnoses of all images so a model could be trained to classify five distinct types of cells.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec id=\"j_bmt-2022-0297_abs_003\">\n                  <jats:title>Results<\/jats:title>\n                  <jats:p>After implementing the proposed architecture on a large dataset of 5 categories of human peripheral white blood cells, it achieved high accuracy than VGG, U-Net and Resnet. We tested our model with WBC images from additional public datasets such as the Kaagel data sets and Raabin data sets of which the accuracy was 98.80% and 98.95%.<\/jats:p>\n               <\/jats:sec>\n               <jats:sec id=\"j_bmt-2022-0297_abs_004\">\n                  <jats:title>Conclusions<\/jats:title>\n                  <jats:p>Considering the large sample sizes, we believe the proposed method can be used for improving the diagnostic performance of clinical blood examinations as well as a promising alternative for machine learning. Test results obtained with the system have been satisfying, with outstanding values for Accuracy, Precision, Recall, Specificity and F1 Score.<\/jats:p>\n               <\/jats:sec>","DOI":"10.1515\/bmt-2022-0297","type":"journal-article","created":{"date-parts":[[2022,10,5]],"date-time":"2022-10-05T17:50:30Z","timestamp":1664992230000},"page":"165-174","source":"Crossref","is-referenced-by-count":8,"title":["An integrated and automated testing approach on Inception Restnet-V3 based on convolutional neural network for leukocytes image classification"],"prefix":"10.1515","volume":"68","author":[{"given":"Silambarasi","family":"Palanivel","sequence":"first","affiliation":[{"name":"Department of Electronics and Communication Engineering, Mahendra Engineering College for Women , Tamil Nadu , India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Viswanathan","family":"Nallasamy","sequence":"additional","affiliation":[{"name":"Department of Electronics and Communication Engineering, Mahendra Engineering College , Tamil Nadu , India"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"374","published-online":{"date-parts":[[2022,10,5]]},"reference":[{"key":"2023033114473814387_j_bmt-2022-0297_ref_001","doi-asserted-by":"crossref","unstructured":"Sharma, M, Ram Janghel, R, Aishwarya, B. 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