LeukSNN: A Novel Spiking Neural Network for Efficient Acute Lymphoblastic Leukemia Diagnosis

Information from the abstract

Acute Lymphoblastic Leukemia (ALL) is a rapidly progressing blood cancer that demands timely and accurate diagnosis, particularly in pediatric patients. Conventional diagnosis relies on manual examination of peripheral blood smear images by hematologists, which is time-consuming, subjective, and prone to error. Although deep learning approaches have demonstrated high accuracy in medical imaging, many existing models are computationally intensive, limiting their use in resource-constrained clinical settings. To address these challenges, we propose LeukSNN, a lightweight spiking neural network (SNN) for automated ALL detection from peripheral blood smear images. Unlike conventional CNNs, SNNs employ sparse event-driven computations that can substantially reduce computational and energy requirements, making them attractive for deployment in low-resource healthcare environments. The proposed architecture combines depthwise separable convolutions, residual connections, and attention mechanisms within an SNN framework to achieve high classification performance with reduced computational cost. Experiments on three publicly available datasets demonstrate accuracies of 99.91–100%, while requiring only 8% and 28% of the multiplication and addition operations, respectively, of current state-of-the-art efficient methods. These results demonstrate that LeukSNN can achieve highly accurate and computationally efficient ALL classification on benchmark datasets, highlighting the potential of SNN-based approaches for future investigation in resource-constrained healthcare environments.

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Related topics: Digital Imaging for Blood Diseases · AI in cancer detection · Acute Myeloid Leukemia Research