ALLD: Acute Lymphoblastic Leukemia Detector.

Acute lymphoblastic leukemia Computer aided diagnosis (CAD) Deep learning Leukemia

Journal

Studies in health technology and informatics
ISSN: 1879-8365
Titre abrégé: Stud Health Technol Inform
Pays: Netherlands
ID NLM: 9214582

Informations de publication

Date de publication:
14 Jan 2022
Historique:
entrez: 22 1 2022
pubmed: 23 1 2022
medline: 27 1 2022
Statut: ppublish

Résumé

Acute Lymphoblastic Leukemia (ALL) is a life-threatening type of cancer wherein mortality rate is unquestionably high. Early detection of ALL can reduce both the rate of fatality as well as improve the diagnosis plan for patients. In this study, we developed the ALL Detector (ALLD), which is a deep learning-based network to distinguish ALL patients from healthy individuals based on blast cell microscopic images. We evaluated multiple DL-based models and the ResNet-based model performed the best with 98% accuracy in the classification task. We also compared the performance of ALLD against state-of-the-art tools utilized for the same purpose, and ALLD outperformed them all. We believe that ALLD will support pathologists to explicitly diagnose ALL in the early stages and reduce the burden on clinical practice overall.

Identifiants

pubmed: 35062096
pii: SHTI210863
doi: 10.3233/SHTI210863
doi:

Types de publication

Journal Article

Langues

eng

Pagination

77-80

Auteurs

Saleh Musleh (S)

College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar.

Mohammad Tariqul Islam (MT)

Computer Science Department, Southern Connecticut State University, New Haven, CT 06515, USA.

Mohammad Towfik Alam (MT)

Department of Vascular Biology and Molecular Pathology, Faculty of Dental Medicine and Graduate School of Dental Medicine, Hokkaido University, Sapporo 060-8586, Japan.

Mowafa Househ (M)

College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar.

Zubair Shah (Z)

College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar.

Tanvir Alam (T)

College of Science and Engineering, Hamad Bin Khalifa University, Doha, Qatar.

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