{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T19:24:31Z","timestamp":1793129071210,"version":"4.5.0"},"reference-count":22,"publisher":"SAGE Publications","issue":"3","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["XST"],"published-print":{"date-parts":[[2022,4,15]]},"abstract":"<jats:p>BACKGROUND: Processing Low-Intensity Medical Images (LI-MI) is difficult as outcomes are varied when it comes to manual examination, which is also a time-consuming process. OBJECTIVE: To improve the quality of low-intensity images and identify the leukemia classification by utilizing CNN-based Deep Learning (DCNN) strategy. METHODS: The strategies employed for the recognition of leukemia classifications in the advised strategy are DCNN (ResNet-34 &amp; DenseNet-121). The histogram equalization-based adaptive gamma correction followed by guided filtering applies to study the improvement in intensity and preserve the essential details of the image. The DCNN is used as a feature extractor to help classify leukemia types. Two datasets of ASH with 520 images and ALL-IDB with 559 images are used in this study. In 1,079 images, 779 are positive cases depicting leukemia and 300 images are negative (normal) cases. Thus, to validate performance of this DCNN strategy, ASH and ALL-IDB datasets are promoted in the investigation process to classify between positive and negative images. RESULTS: The DCNN classifier yieldes the overall classification accuracy of 99.2% and 98.4%, respectively. In addition, the achieved classification specificity, sensitivity, and precision are 99.3%, 98.7%, 98.4%, and 98.9%, 98.4%,98.6% applying to two datasets, respectively, which are higher than the performance using other machine learning classifiers including support vector machine, decision tree, naive bayes, random forest and VGG-16. CONCLUSION: Ths study demonstrates that the proposed DCNN enables to improve low-intensity images and accuracry of leukemia classification, which is superior to many of other machine leaning classifiers used in this research field.<\/jats:p>","DOI":"10.3233\/xst-211055","type":"journal-article","created":{"date-parts":[[2022,3,1]],"date-time":"2022-03-01T13:36:49Z","timestamp":1646141809000},"page":"567-585","source":"Crossref","is-referenced-by-count":16,"title":["Leukemia classification using the deep learning method of CNN"],"prefix":"10.1177","volume":"30","author":[{"given":"B.","family":"Arivuselvam","sequence":"first","affiliation":[{"name":"Department of Electronics and Communication Engineering, Easwari Engineering College, Chennai, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"S.","family":"Sudha","sequence":"additional","affiliation":[{"name":"Department of Electronics and Communication Engineering, Easwari Engineering College, Chennai, 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