{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,9]],"date-time":"2026-10-09T15:46:03Z","timestamp":1791560763424,"version":"4.3.4"},"reference-count":25,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2019,8,25]],"date-time":"2019-08-25T00:00:00Z","timestamp":1566691200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Leukemia is a fatal cancer and has two main types: Acute and chronic. Each type has two more subtypes: Lymphoid and myeloid. Hence, in total, there are four subtypes of leukemia. This study proposes a new approach for diagnosis of all subtypes of leukemia from microscopic blood cell images using convolutional neural networks (CNN), which requires a large training data set. Therefore, we also investigated the effects of data augmentation for an increasing number of training samples synthetically. We used two publicly available leukemia data sources: ALL-IDB and ASH Image Bank. Next, we applied seven different image transformation techniques as data augmentation. We designed a CNN architecture capable of recognizing all subtypes of leukemia. Besides, we also explored other well-known machine learning algorithms such as naive Bayes, support vector machine, k-nearest neighbor, and decision tree. To evaluate our approach, we set up a set of experiments and used 5-fold cross-validation. The results we obtained from experiments showed that our CNN model performance has 88.25% and 81.74% accuracy, in leukemia versus healthy and multi-class classification of all subtypes, respectively. Finally, we also showed that the CNN model has a better performance than other well-known machine learning algorithms.<\/jats:p>","DOI":"10.3390\/diagnostics9030104","type":"journal-article","created":{"date-parts":[[2019,8,26]],"date-time":"2019-08-26T04:38:23Z","timestamp":1566794303000},"page":"104","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":184,"title":["Identification of Leukemia Subtypes from Microscopic Images Using Convolutional Neural Network"],"prefix":"10.3390","volume":"9","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5329-3175","authenticated-orcid":false,"given":"Nizar","family":"Ahmed","sequence":"first","affiliation":[{"name":"Department of Computer Engineering, Dokuz Eylul University, 35160 Izmir, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Altug","family":"Yigit","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Dokuz Eylul University, 35160 Izmir, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1779-1681","authenticated-orcid":false,"given":"Zerrin","family":"Isik","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Dokuz Eylul University, 35160 Izmir, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7695-196X","authenticated-orcid":false,"given":"Adil","family":"Alpkocak","sequence":"additional","affiliation":[{"name":"Department of Computer Engineering, Dokuz Eylul University, 35160 Izmir, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,8,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"635","DOI":"10.1016\/j.procs.2015.08.082","article-title":"Automated leukemia detection using microscopic images","volume":"58","author":"Patel","year":"2015","journal-title":"Procedia Comput. 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