A fast and efficient CNN model for B‐ALL diagnosis and its subtypes classification using peripheral blood smear images

International Journal of Intelligent SystemsVolume 37, Issue 8 p. 5113-5133 RESEARCH ARTICLE A fast and efficient CNN model for B-ALL diagnosis and its subtypes classification using peripheral blood smear images Mustafa Ghaderzadeh, Mustafa Ghaderzadeh orcid.org/0000-0003-4016-3843 Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, IranSearch for more papers by this authorMehrad Aria, Mehrad Aria orcid.org/0000-0002-6027-6952 Department of Information Technology and Computer Engineering, Azarbaijan Shahid Madani University, Tabriz, IranSearch for more papers by this authorAzamossadat Hosseini, Corresponding Author Azamossadat Hosseini Souhosseini@sbmu.ac.ir orcid.org/0000-0002-4390-1154 Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran Correspondence Azamossadat Hosseini and Farkhondeh Asadi, Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran. Email: Souhosseini@sbmu.ac.ir (A.H.) Asadifar@sbmu.ac.ir (F. A.)Search for more papers by this authorFarkhondeh Asadi, Corresponding Author Farkhondeh Asadi Asadifar@sbmu.ac.ir orcid.org/0000-0003-0939-7983 Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran Correspondence Azamossadat Hosseini and Farkhondeh Asadi, Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran. Email: Souhosseini@sbmu.ac.ir (A.H.) Asadifar@sbmu.ac.ir (F. A.)Search for more papers by this authorDavood Bashash, Davood Bashash orcid.org/0000-0002-8029-4920 Department of Hematology and Blood Banking, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, IranSearch for more papers by this authorHassan Abolghasemi, Hassan Abolghasemi orcid.org/0000-0001-6920-5029 Pediatric Congenital Hematologic Disorders Research Department, Shahid Beheshti University of Medical Sciences, Tehran, IranSearch for more papers by this author Mustafa Ghaderzadeh, Mustafa Ghaderzadeh orcid.org/0000-0003-4016-3843 Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, IranSearch for more papers by this authorMehrad Aria, Mehrad Aria orcid.org/0000-0002-6027-6952 Department of Information Technology and Computer Engineering, Azarbaijan Shahid Madani University, Tabriz, IranSearch for more papers by this authorAzamossadat Hosseini, Corresponding Author Azamossadat Hosseini Souhosseini@sbmu.ac.ir orcid.org/0000-0002-4390-1154 Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran Correspondence Azamossadat Hosseini and Farkhondeh Asadi, Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran. Email: Souhosseini@sbmu.ac.ir (A.H.) Asadifar@sbmu.ac.ir (F. A.)Search for more papers by this authorFarkhondeh Asadi, Corresponding Author Farkhondeh Asadi Asadifar@sbmu.ac.ir orcid.org/0000-0003-0939-7983 Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran Correspondence Azamossadat Hosseini and Farkhondeh Asadi, Department of Health Information Technology and Management, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran. Email: Souhosseini@sbmu.ac.ir (A.H.) Asadifar@sbmu.ac.ir (F. A.)Search for more papers by this authorDavood Bashash, Davood Bashash orcid.org/0000-0002-8029-4920 Department of Hematology and Blood Banking, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, IranSearch for more papers by this authorHassan Abolghasemi, Hassan Abolghasemi orcid.org/0000-0001-6920-5029 Pediatric Congenital Hematologic Disorders Research Department, Shahid Beheshti University of Medical Sciences, Tehran, IranSearch for more papers by this author First published: 17 November 2021 https://doi.org/10.1002/int.22753Citations: 2Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Abstract The definitive diagnosis of acute lymphoblastic leukemia (ALL), as a highly prevalent cancer, requires invasive, expensive, and time-consuming diagnostic tests. ALL diagnosis using peripheral blood smear (PBS) images plays a vital role in the initial screening of cancer from non-cancer cases. The examination of these PBS images by laboratory users is riddled with problems such as diagnostic error because the nonspecific nature of ALL signs and symptoms often leads to misdiagnosis. Herein, a model based on deep convolutional neural networks (CNNs) is proposed to detect ALL from hematogone cases and then determine ALL subtypes. In this paper, we build a publicly available ALL data set, comprised 3562 PBS images from 89 patients suspected of ALL, including 25 healthy individuals with a benign diagnosis (hematogone) and 64 patients with a definitive diagnosis of ALL subtypes. After color thresholding-based segmentation in the HSV color space by designing a two-channel network, 10 well-known CNN architectures (EfficientNet, MobileNetV3, VGG-19, Xception, InceptionV3, ResNet50V2, VGG-16, NASNetLarge, InceptionResNetV2, and DenseNet201) were employed for feature extraction of different data classes. Of these 10 models, DenseNet201 achieved the best performance in diagnosis and classification. Finally, a model was developed and proposed based on this state-of-the-art technology. This deep learning-based model attained an accuracy, sensitivity, and specificity of 99.85, 99.52, and 99.89%, respectively. The proposed method may help to distinguish ALL from benign cases. This model is also able to assist hematologists and laboratory personnel in diagnosing ALL subtypes and thus determining the treatment protocol associated with these subtypes. The proposed data set is available at https://www.kaggle.com/mehradaria/leukemia and the implementation (source code) of proposed method is made publicly available at https://github.com/MehradAria/ALL-Subtype-Classification. Citing Literature Volume37, Issue8August 2022Pages 5113-5133 RelatedInformation