Multi-Scale CBAM Fusion Network: A Lightweight and Explainable Deep Learning Approach for Acute Lymphoblastic Leukemia Detection from Blood Smear Images.

This work proposes MSCBAMFN, a lightweight (0.91M-parameter) network that matches or outperforms four much larger ImageNet-pretrained backbones (ResNet50, EfficientNetB0/B3, DenseNet121) with 4–27× more parameters, achieving 99.59% accuracy on the original dataset and 96.94% on the segmented dataset. The model also shows strong clinical reliability, missing only 2 of 490 malignant cases on the original dataset, which matters most in screening where a missed diagnosis is far costlier than a false alarm. Grad-CAM applied at the fusion layer confirms the model’s explainability by consistently highlighting the nucleus and cytoplasmic boundary as the regions driving its predictions, rather than background artifacts. Since the segmented dataset is a background-removed, cell-focused version of the same original images, the model’s consistent performance across both variants demonstrates strong generalization based on genuine cell morphology rather than incidental background cues. This is further validated by a component-wise ablation study confirming the contribution of CBAM and multi-scale fusion, along with 5-fold stratified cross-validation (99.11%±0.23% and 97.02%±0.86%) showing the results are stable and reproducible rather than an artifact of a single data split.