Citation

Rahman, Syed Ijaz Ur and Abbas, Naveed and Ali, Sikandar and Jan, Salman and Lee, It Ee and Salman, Muhammad and Salhi, Amina and Alkhyyat, Ahmed (2026) Improving acute lymphoblastic leukemia diagnosis through CBAM-enhanced VGG19 deep learning. Scientific Reports, 16 (1). ISSN 2045-2322

Text s41598-026-40184-4.pdf - Published Version Restricted to Repository staff only Download (2MB)

Abstract

Acute Lymphoblastic Leukemia (ALL) is an aggressive blood cancer that requires rapid and accurate diagnosis. Manual review of blood and bone marrow smears is time-consuming and observerdependent, underscoring the need for automated methods. This study presents a deep learning framework with attention mechanisms for automated detection and subtyping of ALL from microscopic bone marrow images, including healthy samples. The model combines a Convolutional Block Attention Module (CBAM) with a VGG19 backbone, forming a hybrid CBAM–VGG19 network that hierarchically enhances key morphological features across spatial and channel dimensions. Incorporating CBAM into VGG19 improves feature extraction, accelerates learning, and boosts classification accuracy, particularly for visually similar leukemia subtypes. The model’s performance was validated using k-fold cross-validation, achieving 98.73% classification accuracy, surpassing DenseNet121, InceptionV3, MobileNetV2, and the original VGG19. Further analyses examined the effects of image resolution, hyperparameter optimization, Bayesian tuning, and CBAM layer placement, all of which improved convergence and robustness while reducing overfitting. Although the results are promising, the lack of external validation and the small dataset size limit clinical applicability. Therefore, this framework is a research prototype intended to serve as a basis for future large-scale, multi-center studies.

Item Type:	Article
Uncontrolled Keywords:	Acute lymphoblastic leukemia, deep learning, segmentation
Subjects:	R Medicine > R Medicine (General) > R858-859.7 Computer applications to medicine. Medical informatics
Divisions:	Faculty of Artificial Intelligence & Engineering (FAIE)
Depositing User:	Ms Rosnani Abd Wahab
Date Deposited:	04 May 2026 06:30
Last Modified:	07 May 2026 08:48
URII:	http://shdl.mmu.edu.my/id/eprint/15888

Downloads

Edit (login required)