ArticleFrontiers in artificial intelligence2026
SPiKe-Med: a hybrid spiking-CNN-transformer framework for energy-efficient brain tumor MRI segmentation.
Article in Frontiers in artificial intelligence, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
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,
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