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Acute Lymphoblastic Leukemia (ALL) spreads out in children's bodies rapidly and takes the life within a few weeks. To diagnose ALL, the hematologists perform blood and bone marrow examination. Manual blood testing techniques that have been used since long time are often slow and come out with the less accurate diagnosis. This work improves the diagnosis of ALL with a computer\u2010aided system, which yields accurate result by using image processing and deep learning techniques. This research proposed a method for the classification of ALL into its subtypes and reactive bone marrow (normal) in stained bone marrow images. A robust segmentation and deep learning techniques with the convolutional neural network are used to train the model on the bone marrow images to achieve accurate classification results. Experimental results thus obtained and compared with the results of other classifiers Na\u00efve Bayesian, KNN, and SVM. Experimental results reveal that the proposed method achieved 97.78% accuracy. The obtained results exhibit that the proposed approach could be used as a tool to diagnose Acute Lymphoblastic Leukemia and its sub\u2010types that will definitely assist pathologists.<\/jats:p>","DOI":"10.1002\/jemt.23139","type":"journal-article","created":{"date-parts":[[2018,10,23]],"date-time":"2018-10-23T20:48:19Z","timestamp":1540327699000},"page":"1310-1317","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":282,"title":["Classification of acute lymphoblastic leukemia using deep learning"],"prefix":"10.1002","volume":"81","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-3817-2655","authenticated-orcid":false,"given":"Amjad","family":"Rehman","sequence":"first","affiliation":[{"name":"College of Computer and Information Systems Al Yamamah University  Riyadh Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Naveed","family":"Abbas","sequence":"additional","affiliation":[{"name":"Department of Computer Science Islamia College University Peshawar  Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3138-3801","authenticated-orcid":false,"given":"Tanzila","family":"Saba","sequence":"additional","affiliation":[{"name":"College of Computer and Information Sciences Prince Sultan University  Riyadh Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Syed Ijaz ur","family":"Rahman","sequence":"additional","affiliation":[{"name":"Department of Computer Science Islamia College University Peshawar  Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zahid","family":"Mehmood","sequence":"additional","affiliation":[{"name":"Department of Software Engineering University of Engineering and Technology Taxila  Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hoshang","family":"Kolivand","sequence":"additional","affiliation":[{"name":"Department of Computer Science Liverpool John Moores University  Liverpool United Kingdom"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2018,10,23]]},"reference":[{"key":"e_1_2_8_2_1","doi-asserted-by":"publisher","DOI":"10.19026\/rjaset.7.631"},{"issue":"5","key":"e_1_2_8_3_1","first-page":"1801","article-title":"Nuclei segmentation of leukocytes in blood smear digital images","volume":"28","author":"Abbas N.","year":"2015","journal-title":"Pakistan Journal of Pharmaceutical Sciences"},{"issue":"3","key":"e_1_2_8_4_1","first-page":"1","article-title":"Machine aided malaria parasitemia detection in Giemsa\u2010stained thin blood smears","volume":"29","author":"Abbas N.","year":"2016","journal-title":"Neural Computing and Applications"},{"key":"e_1_2_8_5_1","doi-asserted-by":"crossref","unstructured":"Abd Halim N. 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