Automatic classification of acute lymphoblastic leukemia cells and lymphocyte subtypes based on a novel convolutional neural network.

Autor: MoradiAmin M; Department of Physiology, Faculty of Medicine, AJA University of Medical Sciences, Tehran, Iran.; Department of Medical Physics and Biomedical Engineering, School of Medicine, Tehran University of Medical Sciences (TUMS), Tehran, Iran., Yousefpour M; Department of Physiology, Faculty of Medicine, AJA University of Medical Sciences, Tehran, Iran., Samadzadehaghdam N; Department of Biomedical Engineering, Faculty of Advanced Medical Sciences, Tabriz University of Medical Sciences, Tabriz, Iran., Ghahari L; Department of Anatomy, Faculty of Medicine, AJA University of Medical Sciences, Tehran, Iran., Ghorbani M; Department of Medical Laboratory Sciences, School of Allied Medical Sciences, AJA University of Medical Sciences, Tehran, Iran.; Medical Biotechnology Research Center, AJA University of Medical Sciences, Tehran, Iran., Mafi M; Mechanical Engineering Department, Iran University of Science and Technology, Tehran, Iran.
Jazyk: angličtina
Zdroj: Microscopy research and technique [Microsc Res Tech] 2024 Jul; Vol. 87 (7), pp. 1615-1626. Date of Electronic Publication: 2024 Mar 06.
DOI: 10.1002/jemt.24551
Abstrakt: Acute lymphoblastic leukemia (ALL) is a life-threatening disease that commonly affects children and is classified into three subtypes: L1, L2, and L3. Traditionally, ALL is diagnosed through morphological analysis, involving the examination of blood and bone marrow smears by pathologists. However, this manual process is time-consuming, laborious, and prone to errors. Moreover, the significant morphological similarity between ALL and various lymphocyte subtypes, such as normal, atypic, and reactive lymphocytes, further complicates the feature extraction and detection process. The aim of this study is to develop an accurate and efficient automatic system to distinguish ALL cells from these similar lymphocyte subtypes without the need for direct feature extraction. First, the contrast of microscopic images is enhanced using histogram equalization, which improves the visibility of important features. Next, a fuzzy C-means clustering algorithm is employed to segment cell nuclei, as they play a crucial role in ALL diagnosis. Finally, a novel convolutional neural network (CNN) with three convolutional layers is utilized to classify the segmented nuclei into six distinct classes. The CNN is trained on a labeled dataset, allowing it to learn the distinguishing features of each class. To evaluate the performance of the proposed model, quantitative metrics are employed, and a comparison is made with three well-known deep networks: VGG-16, DenseNet, and Xception. The results demonstrate that the proposed model outperforms these networks, achieving an approximate accuracy of 97%. Moreover, the model's performance surpasses that of other studies focused on 6-class classification in the context of ALL diagnosis. RESEARCH HIGHLIGHTS: Deep neural networks eliminate the requirement for feature extraction in ALL classification The proposed convolutional neural network achieves an impressive accuracy of approximately 97% in classifying six ALL and lymphocyte subtypes.
(© 2024 Wiley Periodicals LLC.)
Databáze: MEDLINE
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