{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T21:18:12Z","timestamp":1770067092287,"version":"3.49.0"},"reference-count":0,"publisher":"Slovenian Association Informatika","issue":"5","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJCAI"],"abstract":"<jats:p>Autism Spectrum Disorder (ASD) diagnosis remains challenging because of its heterogeneity and reliance on subjective behavioral assessments. Resting-state functional MRI (fMRI) presents a compelling opportunity avenue for identifying objective biomarkers, but decoding its complex spatiotemporal patterns requires advanced computational models. While Deep Learning (DL) approaches have progressed, many struggle to concurrently capture local neural dynamics and global temporal dependencies. A novel end-to-end CNN-Transformer hybrid framework designed for fMRI-based autism diagnosis is proposed to address this. Our model leverages a convolutional module to extract localized spatiotemporal features, which are then processed by a Transformer encoder to model long-range, global dependencies through a Multi-Head Self-Attention (MHSA) mechanism. Evaluated on the large multi-site ABIDE-I dataset (N=1,035), the suggested model achieved state-of-the-art performance with an accuracy of 77.85%, a sensitivity of 76.52%, a specificity of 78.90%, and an F1-score of 77.71%. Ablation studies confirmed the critical contribution of each architectural component, and comparisons with pre-trained CNNs and other leading methods demonstrated superior and statistically significant performance (p&lt;0.05). Despite an observed performance drop in site-specific evaluations, underscoring the challenge of scanner heterogeneity, our results affirm that the synergistic integration of local feature learning and global contextual modeling is a powerful paradigm for neuroimaging-based diagnostic applications.<\/jats:p>","DOI":"10.31449\/inf.v50i5.12777","type":"journal-article","created":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T10:25:18Z","timestamp":1770027918000},"source":"Crossref","is-referenced-by-count":0,"title":["Leveraging Local and Global Features: A CNN-Transformer Hybrid for fMRI-Based Autism Diagnosis"],"prefix":"10.31449","volume":"50","author":[{"given":"Mengzhu","family":"Yu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"16141","published-online":{"date-parts":[[2026,2,2]]},"container-title":["Informatica"],"original-title":[],"link":[{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/download\/12777\/6416","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/download\/12777\/6416","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,2]],"date-time":"2026-02-02T10:25:19Z","timestamp":1770027919000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/view\/12777"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,2]]},"references-count":0,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2026,2,2]]}},"URL":"https:\/\/doi.org\/10.31449\/inf.v50i5.12777","relation":{},"ISSN":["1854-3871","0350-5596"],"issn-type":[{"value":"1854-3871","type":"electronic"},{"value":"0350-5596","type":"print"}],"subject":[],"published":{"date-parts":[[2026,2,2]]}}}