@inproceedings{316dfa832d5446f0a969d5c71f5728e1,
title = "ALLD: Acute Lymphoblastic Leukemia Detector",
abstract = "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.",
keywords = "Acute lymphoblastic leukemia, Computer aided diagnosis (CAD), Deep learning, Leukemia",
author = "Saleh Musleh and Islam, {Mohammad Tariqul} and Alam, {Mohammad Towfik} and Mowafa Househ and Zubair Shah and Tanvir Alam",
note = "Publisher Copyright: {\textcopyright} 2022 The authors and IOS Press.",
year = "2022",
doi = "10.3233/SHTI210863",
language = "English",
series = "Studies in Health Technology and Informatics",
publisher = "IOS Press BV",
pages = "77--80",
editor = "John Mantas and Arie Hasman and Househ, {Mowafa S.} and Parisis Gallos and Emmanouil Zoulias and Joseph Liasko",
booktitle = "Informatics and Technology in Clinical Care and Public Health",
address = "Netherlands",
}