NeurALLNet: An attention-based spiking neural network for energy-efficient multi-class classification of acute lymphoblastic leukemia.

Objectives: The classification of Acute Lymphoblastic Leukemia (ALL) from peripheral blood smear images using Convolutional Neural Networks (CNNs) has achieved expert-level accuracy. However, the computational and memory requirements of CNNs pose a barrier to their deployment in resource-constrained clinical settings and low-income countries. To bridge this gap, we propose NeurALLNet, a memory-efficient convolutional spiking neural network (SNN) augmented with Squeeze-and-Excitation channel attention for the multi-class classification of ALL subtypes. Methods: NeurALLNet leverages sparse, event-driven temporal computation with an ultra-compact architecture of approximately 0.3M trainable parameters. The model was trained and evaluated on a primary dataset of ALL peripheral blood smear images, and its clinical generalizability was rigorously validated on an unseen external cohort of 3,242 images without retraining. We conducted hardware profiling on CPU and GPU platforms, alongside ablation studies and Grad-CAM visual explanations, to evaluate deployment viability and interpretability. Results: NeurALLNet achieved a test accuracy of 98.16% on the primary dataset, with a bootstrapped 95% Confidence Interval (CI) of [0.9663, 0.9939]. On the external validation cohort, it yielded an accuracy of 96.02%, with a robust 95% CI of [0.9534, 0.9667]. The architecture requires a memory footprint of 1.35 MB, achieving single-image inference latencies of 454.67 ms on a standard CPU and 11.24 ms on a GPU. Ablation studies confirmed that the attention mechanism is critical to the network's discriminative power, and Grad-CAM visualizations verified that predictions are grounded in clinically relevant morphological features. Conclusion: Compared to recent state-of-the-art ensemble and hybrid CNNs that require millions of parameters, NeurALLNet delivers competitive diagnostic accuracy while reducing the computational footprint by orders of magnitude. By providing this precision within a 1.35 MB envelope, NeurALLNet offers a scalable, energy-efficient digital health intervention suitable for portable Lab-on-a-Chip devices and point-of-care diagnostics worldwide.