SPiKe-Med: a hybrid spiking-CNN-transformer framework for energy-efficient brain tumor MRI segmentation

Early disease detection is crucial for any disease it helps in better treatment and curing. For this purpose, several Artificial Intelligence (AI)-assisted research studies have been developed and tested for medical image processing. Although traditional Deep Learning (DL) models aid in disease detection, computational complexity and energy-intensive challenges limit the performance. For this reason, this research proposes SPiKe-Med, an efficient, accurate and neuromorphic-inspired solution by incorporating Spiking Neural Networks (SNNs) into state-of-the-art Convolutional Neural Network (CNN) and transformer backbones architectures. Stage 1 uses standardized preprocessing, N4 bias-field correction, isotropic resampling, z-score intensity normalization, and modality-specific augmentations. Stage 2 proposes Swin Unet-based Transformer-Network (SUT-Net), a high-performance dense segmentation backbone configured using benchmark Swin Transformer and UNET- Transformers (UNETR) for long-range context. Stage 3 develops the proposed SPiKe-Med by training multi-scale feature maps into spike trains with a learned encoder (temporal encoding) and feeds them to a spiking refinement network of Leaky Integrate-and-Fire (LIF) neurons trained as reconfigurable with SpikingJelly. Stage 4 trains the proposed SPiKe-Med on a two-track schedule: end-to-end surrogate-gradient optimization of SNN components (surrogate derivatives) and selective ANN to SNN porting of pretrained CNN encoder weights to ultra-low-latency inference. Loss functions combine a Dice, focal and temporal consistency term, which is optimized by temperature scaling. The proposed SPiKe-Med is tested with multiple benchmark datasets for different modalities like MRI and CT using Dice, Hausdorff distance, spike-rate and estimated energy per inference.