{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T06:07:45Z","timestamp":1788934065418,"version":"build-2803163510"},"reference-count":38,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T00:00:00Z","timestamp":1788912000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Artif. Intell."],"abstract":"<jats:p>\n                    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,\n                    <jats:italic>z<\/jats:italic>\n                    -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.\n                  <\/jats:p>","DOI":"10.3389\/frai.2026.1852940","type":"journal-article","created":{"date-parts":[[2026,9,9]],"date-time":"2026-09-09T06:00:21Z","timestamp":1788933621000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["SPiKe-Med: a hybrid spiking-CNN-transformer framework for energy-efficient brain tumor MRI segmentation"],"prefix":"10.3389","volume":"9","author":[{"given":"M.","family":"Sujatha","sequence":"first","affiliation":[{"name":"Vellore Institute of Technology - Chennai Campus","place":["Chennai, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"B.","family":"Aarthi","sequence":"additional","affiliation":[{"name":"Vellore Institute of Technology - Chennai Campus","place":["Chennai, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,9]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"78","DOI":"10.1186\/s12911-023-02174-8","article-title":"An early detection and segmentation of brain tumor using deep neural network","volume":"23","author":"Aggarwal","year":"2023","journal-title":"BMC Med. 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