A reliable approach for identifying acute lymphoblastic leukemia in microscopic imaging
Leukemia is a deadly disease, and the patient’s recovery rate is very dependent on early diagnosis. However, its diagnosis under the microscope is tedious and time-consuming. The advancement of deep convolutional neural networks (CNNs) in image classification has enabled new techniques in automated...
Saved in:
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2025-07-01
|
| Series: | Frontiers in Artificial Intelligence |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/frai.2025.1620252/full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850101282720710656 |
|---|---|
| author | Mimosette Makem Levente Tamas Lucian Bușoniu |
| author_facet | Mimosette Makem Levente Tamas Lucian Bușoniu |
| author_sort | Mimosette Makem |
| collection | DOAJ |
| description | Leukemia is a deadly disease, and the patient’s recovery rate is very dependent on early diagnosis. However, its diagnosis under the microscope is tedious and time-consuming. The advancement of deep convolutional neural networks (CNNs) in image classification has enabled new techniques in automated disease detection systems. These systems serve as valuable support and secondary opinion resources for laboratory technicians and hematologists when diagnosing leukemia through microscopic examination. In this study, we deployed a pre-trained CNN model (MobileNet) that has a small size and low complexity, making it suitable for mobile applications and embedded systems. We used the L1 regularization method and a novel dataset balancing approach, which incorporates HSV color transformation, saturation elimination, Gaussian noise addition, and several established augmentation techniques, to prevent model overfitting. The proposed model attained an accuracy of 95.33% and an F1 score of 0.95 when evaluated on the held-out test set extracted from the C_NMC_2019 public dataset. We also evaluated the proposed model by adding zero-mean Gaussian noise to the test images. The experimental results indicate that the proposed model is both efficient and robust, even when subjected to additional Gaussian noise. The comparison of the proposed MobileNet_M model’s results with those of ALNet and various other existing models on the same dataset underscores its superior efficacy. The code is available for reproducing the experimental results at https://tamaslevente.github.io/ALLM/. |
| format | Article |
| id | doaj-art-7d20dbf82efa4791a3253b07f0d2a178 |
| institution | DOAJ |
| issn | 2624-8212 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Artificial Intelligence |
| spelling | doaj-art-7d20dbf82efa4791a3253b07f0d2a1782025-08-20T02:40:04ZengFrontiers Media S.A.Frontiers in Artificial Intelligence2624-82122025-07-01810.3389/frai.2025.16202521620252A reliable approach for identifying acute lymphoblastic leukemia in microscopic imagingMimosette Makem0Levente Tamas1Lucian Bușoniu2Signal, Image, and Systems Laboratory, Department of Medical and Biomedical Engineering, HTTTC EBOLOWA, University of Ebolowa, Ebolowa, CameroonDepartment of Automation, Technical University of Cluj-Napoca, Cluj-Napoca, RomaniaDepartment of Automation, Technical University of Cluj-Napoca, Cluj-Napoca, RomaniaLeukemia is a deadly disease, and the patient’s recovery rate is very dependent on early diagnosis. However, its diagnosis under the microscope is tedious and time-consuming. The advancement of deep convolutional neural networks (CNNs) in image classification has enabled new techniques in automated disease detection systems. These systems serve as valuable support and secondary opinion resources for laboratory technicians and hematologists when diagnosing leukemia through microscopic examination. In this study, we deployed a pre-trained CNN model (MobileNet) that has a small size and low complexity, making it suitable for mobile applications and embedded systems. We used the L1 regularization method and a novel dataset balancing approach, which incorporates HSV color transformation, saturation elimination, Gaussian noise addition, and several established augmentation techniques, to prevent model overfitting. The proposed model attained an accuracy of 95.33% and an F1 score of 0.95 when evaluated on the held-out test set extracted from the C_NMC_2019 public dataset. We also evaluated the proposed model by adding zero-mean Gaussian noise to the test images. The experimental results indicate that the proposed model is both efficient and robust, even when subjected to additional Gaussian noise. The comparison of the proposed MobileNet_M model’s results with those of ALNet and various other existing models on the same dataset underscores its superior efficacy. The code is available for reproducing the experimental results at https://tamaslevente.github.io/ALLM/.https://www.frontiersin.org/articles/10.3389/frai.2025.1620252/fullleukemia classificationimage processingCNNdisease detectiondata augmentation |
| spellingShingle | Mimosette Makem Levente Tamas Lucian Bușoniu A reliable approach for identifying acute lymphoblastic leukemia in microscopic imaging Frontiers in Artificial Intelligence leukemia classification image processing CNN disease detection data augmentation |
| title | A reliable approach for identifying acute lymphoblastic leukemia in microscopic imaging |
| title_full | A reliable approach for identifying acute lymphoblastic leukemia in microscopic imaging |
| title_fullStr | A reliable approach for identifying acute lymphoblastic leukemia in microscopic imaging |
| title_full_unstemmed | A reliable approach for identifying acute lymphoblastic leukemia in microscopic imaging |
| title_short | A reliable approach for identifying acute lymphoblastic leukemia in microscopic imaging |
| title_sort | reliable approach for identifying acute lymphoblastic leukemia in microscopic imaging |
| topic | leukemia classification image processing CNN disease detection data augmentation |
| url | https://www.frontiersin.org/articles/10.3389/frai.2025.1620252/full |
| work_keys_str_mv | AT mimosettemakem areliableapproachforidentifyingacutelymphoblasticleukemiainmicroscopicimaging AT leventetamas areliableapproachforidentifyingacutelymphoblasticleukemiainmicroscopicimaging AT lucianbusoniu areliableapproachforidentifyingacutelymphoblasticleukemiainmicroscopicimaging AT mimosettemakem reliableapproachforidentifyingacutelymphoblasticleukemiainmicroscopicimaging AT leventetamas reliableapproachforidentifyingacutelymphoblasticleukemiainmicroscopicimaging AT lucianbusoniu reliableapproachforidentifyingacutelymphoblasticleukemiainmicroscopicimaging |