What question did this study set out to answer?

The research aims to enhance the classification of Acute Lymphoblastic Leukemia using advanced deep learning techniques.

May 6, 2026Open Access

A Lightweight Hybrid CNN–CBAM Model for Multistage Acute Lymphoblastic Leukemia Classification from Peripheral Blood Smear Images

Key Points

The research aims to enhance the classification of Acute Lymphoblastic Leukemia using advanced deep learning techniques.
Developed a lightweight hybrid model integrating EfficientNetV2-S and Convolutional Block Attention Modules (CBAM).
Implemented Focal Loss to address class imbalance in the dataset.
Utilized a robust preprocessing pipeline for image enhancement and data augmentation.
Conducted lesion segmentation using RGB-based thresholding and watershed overlay.
Achieved an average accuracy of 96.11% and an F1-score of 95.99%.
Demonstrated superior performance compared to conventional CNN architectures.
Identified RGB as the most effective color space for segmentation and classification.

Abstract

Accurate and efficient classification of hematological malignancies from peripheral blood smear (PBS) images remains challenging due to the scarcity of annotated datasets, staining variability, and subtle morphological differences among blood cancer subtypes. To address these limitations, this study proposes an Advanced Lightweight Deep Learning (ALDL) framework for the multi-class classification of Acute Lymphoblastic Leukemia (ALL) across four clinically significant stages: Benign, Pro-B, Pre-B, and Early Pre-B. The framework integrates EfficientNetV2-S with Convolutional Block Attention Modules (CBAM) to enhance spatial and channel-wise feature refinement. At the same time, Focal Loss is employed to mitigate class imbalance by prioritizing hard-to-classify samples. A robust preprocessing pipeline, including CLAHE contrast enhancement, Reinhard stain normalization, and data augmentation, improves feature visibility and dataset generalization. Lesion segmentation is performed using RGB-based thresholding and watershed overlay, followed by lesion-level cropping to ensure consistency across inputs. Experimental evaluations on the ALL-DB dataset demonstrate the superior performance of the proposed method, achieving an average accuracy of 96.11%, an F1-score of 95.99%, and an AUC of 0.9875. Comparative analyses against MobileNetV3, ResNet50, DenseNet121, VGG16, and InceptionV3 confirm that the proposed segmentation-guided EfficientNetV2-S + CBAM + Focal Loss framework consistently outperforms conventional CNN architectures across both 70:30 and 60:40 train–test splits. Furthermore, a detailed investigation of color spaces (RGB, HSV, LAB, and HED) indicates that RGB yields the most reliable segmentation and classification results. At the same time, HED enhances lesion visualization at the expense of higher computational cost. The proposed ALDL framework demonstrates strong potential for real-world application as a computer-aided diagnostic (CAD) system for early leukemia detection, offering improved diagnostic reliability, reduced error rates, and practical scalability for clinical environments.

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