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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. © 2022 The authors and IOS Press.

0 citations (Crossref) [2023-10-31] ISBN: 9781643682501 tex.author_keywords: Acute lymphoblastic leukemia; Computer aided diagnosis (CAD); Deep learning; Leukemia tex.document_type: Conference Paper tex.pubmed_id: 35062096 tex.source: Scopus