B-cell acute lymphoblastic leukemia (B-ALL) is an aggressive hematological malignancy that primarily affects children but can also occur in adults, progressing rapidly and requiring urgent clinical intervention. Late-stage diagnosis often results in reduced survival rates and typically depends on costly, time-intensive diagnostic procedures. Peripheral blood smear (PBS) imaging plays a central role in the preliminary screening of B-ALL and provides an accessible foundation for computer-assisted diagnosis. To support early and efficient classification, this study proposes a lightweight convolutional neural network (CNN) designed to classify B-ALL subtypes directly from PBS images without the need for pre-segmentation. The model is computationally efficient, comprising only 986,126 trainable parameters, and integrates Squeeze-and-Excitation (SE) modules within Inverted Residual Blocks to strengthen feature representation. Experimental results demonstrated excellent classification performance, achieving 100 % accuracy, precision, sensitivity, specificity, F1-score, and Matthews correlation coefficient (MCC). To further assess generalizability, cross-dataset validation was performed on the independent Blood Cells Cancer (ALL) dataset without retraining or fine-tuning, yielding a robust accuracy of 99.85 %. Model interpretability was performed using Gradient-weighted Class Activation Mapping (Grad-CAM) and Local Interpretable Model-agnostic Explanations (LIME), which provided visual explanations and highlighted key discriminative cellular features, respectively. Taken together, these results demonstrate that the proposed framework delivers a highly accurate, resource-efficient, and interpretable solution for B-ALL classification, underscoring its strong potential for integration into real-world clinical practice. Additionally, the implementation code for this study is publicly available at: https://github.com/awazabbas/Efficient-Lightweight-CNN-for-Automated-Classification-of-B-cell-Acute-Lymphoblastic-Leukemia.
