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Advancing early leukemia diagnostics: a comprehensive study incorporating image processing and transfer learning.

Haque, Rezaul; Al Sakib, Abdullah; Hossain, Md Forhad; Islam, Fahadul; Aziz, Ferdaus Ibne; Ahmed, Md Redwan; Kannan, Somasundar; Rohan, Ali; Hasan, Md Junayed

Authors

Rezaul Haque

Abdullah Al Sakib

Md Forhad Hossain

Fahadul Islam

Ferdaus Ibne Aziz

Md Redwan Ahmed



Abstract

Disease recognition has been revolutionized by autonomous systems in the rapidly developing field of medical technology. A crucial aspect of diagnosis involves the visual assessment and enumeration of white blood cells in microscopic peripheral blood smears. This practice yields invaluable insights into a patient's health, enabling the identification of conditions of blood malignancies such as leukemia. Early identification of leukemia subtypes is paramount for tailoring appropriate therapeutic interventions and enhancing patient survival rates. However, traditional diagnostic techniques, which depend on visual assessment, are arbitrary, laborious, and prone to errors. The advent of ML technologies offers a promising avenue for more accurate and efficient leukemia classification. In this study, we introduced a novel approach to leukemia classification by integrating advanced image processing, diverse dataset utilization, and sophisticated feature extraction techniques, coupled with the development of TL models. Focused on improving accuracy of previous studies, our approach utilized Kaggle datasets for binary and multiclass classifications. Extensive image processing involved a novel LoGMH method, complemented by diverse augmentation techniques. Feature extraction employed DCNN, with subsequent utilization of extracted features to train various ML and TL models. Rigorous evaluation using traditional metrics revealed Inception-ResNet's superior performance, surpassing other models with F1 scores of 96.07% and 95.89% for binary and multiclass classification, respectively. Our results notably surpass previous research, particularly in cases involving a higher number of classes. These findings promise to influence clinical decision support systems, guide future research, and potentially revolutionize cancer diagnostics beyond leukemia, impacting broader medical imaging and oncology domains.

Citation

HAQUE, R., AL SAKIB, A., HOSSAIN, M.F., ISLAM, F., AZIZ, F.I., AHMED, M.R., KANNAN, S., ROHAN, A. and HASAN, M.J. 2024. Advancing early leukemia diagnostics: a comprehensive study incorporating image processing and transfer learning. BioMedInformatics [online], 4(2), pages 966-991. Available from: https://doi.org/10.3390/biomedinformatics4020054

Journal Article Type Article
Acceptance Date Mar 25, 2024
Online Publication Date Apr 1, 2024
Publication Date Jun 30, 2024
Deposit Date Apr 9, 2024
Publicly Available Date May 10, 2024
Journal BioMedInformatics
Print ISSN 2673-7426
Electronic ISSN 2673-7426
Publisher MDPI
Peer Reviewed Peer Reviewed
Volume 4
Issue 2
Pages 966-991
DOI https://doi.org/10.3390/biomedinformatics4020054
Keywords Image processing; Machine learning; Medical imaging; Leukemia; Disease detection; Medicine and technology
Public URL https://rgu-repository.worktribe.com/output/2294340

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