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Magnetic resonance imaging (MRI) is the clinical standard for noninvasive brain tumour assessment; however, manual interpretation is time\u2010intensive and susceptible to interobserver variability. To address these challenges, this study presents Neuro\u2010Oncology Artificial Intelligence Assisted Support and Interpretation System (NeuroAssist), a hybrid artificial intelligence (AI) framework built upon Squeeze\u2010and\u2010Excitation Mobile Network (SEMoNet) for automated BTC, coupled with a patient support chatbot to enhance interpretability and engagement. SEMoNet integrates Mobile Network Version 2 (MobileNetV2) for computationally efficient feature extraction with Squeeze\u2010and\u2010Excitation ResNet50 (SE\u2010ResNet50) for attention\u2010driven deep feature representation. The fusion of lightweight and attention\u2010enhanced architecture enables robust learning of both local and global tumour characteristics. The proposed framework classifies MRI scans into four clinically relevant categories: no tumour, glioma, meningioma and pituitary tumour. Experiments conducted on a curated dataset of 7200 MRI images demonstrate that SEMoNet achieves a classification accuracy of 93.2%, outperforming several established convolutional architectures in terms of accuracy, precision, recall, F1 score and AUC\u2010ROC. To enhance clinical usability and patient\u2010centred care, NeuroAssist incorporates a ChatGPT\u2010powered patient support chatbot (PSC), which translates model predictions into clear, patient\u2010friendly explanations and provides nondiagnostic, supportive guidance for personalized health planning. This integration bridges the gap between AI\u2010driven diagnostics and human\u2010centred communication. The results confirm that NeuroAssist offers a balanced combination of diagnostic accuracy, computational efficiency, interpretability and scalability, highlighting its potential for sustainable deployment in real\u2010world neuro\u2010oncology workflows and resource\u2010constrained clinical environments.<\/jats:p>","DOI":"10.1155\/acis\/7216102","type":"journal-article","created":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T02:37:26Z","timestamp":1773023846000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["NeuroAssist: A Hybrid SEMoNet Framework for Brain Tumour Classification From MRI With Patient\u2010Centred Conversational Support"],"prefix":"10.1155","volume":"2026","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5269-0427","authenticated-orcid":false,"given":"Fahad","family":"Ahmad","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ayesha","family":"Khaliq","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Umer","family":"Fareed","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Adeel","family":"Aslam","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5328-4105","authenticated-orcid":false,"given":"Kashaf","family":"Junaid","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2026,2,23]]},"reference":[{"key":"e_1_2_14_1_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.irbm.2021.06.003"},{"key":"e_1_2_14_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/tts.2023.3234203"},{"key":"e_1_2_14_3_2","doi-asserted-by":"publisher","DOI":"10.1007\/s11831-022-09758-z"},{"key":"e_1_2_14_4_2","doi-asserted-by":"publisher","DOI":"10.3390\/pr11010212"},{"key":"e_1_2_14_5_2","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-023-50505-6"},{"key":"e_1_2_14_6_2","doi-asserted-by":"publisher","DOI":"10.1109\/access.2022.3184113"},{"key":"e_1_2_14_7_2","doi-asserted-by":"publisher","DOI":"10.3390\/life13010024"},{"key":"e_1_2_14_8_2","doi-asserted-by":"publisher","DOI":"10.58344\/jws.v2i10.449"},{"key":"e_1_2_14_9_2","doi-asserted-by":"publisher","DOI":"10.3390\/electronics11071146"},{"key":"e_1_2_14_10_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jbi.2022.104071"},{"key":"e_1_2_14_11_2","first-page":"881","article-title":"Towards More Patient Friendly Clinical Notes Through Language Models and Ontologies","volume":"2021","author":"Moramarco F.","year":"2022","journal-title":"AMIA Annual Symposium Proceedings"},{"key":"e_1_2_14_12_2","doi-asserted-by":"publisher","DOI":"10.53730\/ijhs.v7ns1.14330"},{"key":"e_1_2_14_13_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpbup.2024.100146"},{"key":"e_1_2_14_14_2","doi-asserted-by":"publisher","DOI":"10.48175\/ijarsct-22985"},{"key":"e_1_2_14_15_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.semcancer.2023.07.002"},{"key":"e_1_2_14_16_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-022-07742-z"},{"key":"e_1_2_14_17_2","doi-asserted-by":"publisher","DOI":"10.3390\/app12115645"},{"key":"e_1_2_14_18_2","doi-asserted-by":"publisher","DOI":"10.1109\/access.2022.3154061"},{"key":"e_1_2_14_19_2","doi-asserted-by":"publisher","DOI":"10.1109\/access.2022.3179376"},{"key":"e_1_2_14_20_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.dajour.2024.100461"},{"key":"e_1_2_14_21_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.jpse.2022.100091"},{"key":"e_1_2_14_22_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ndteint.2022.102635"},{"key":"e_1_2_14_23_2","doi-asserted-by":"publisher","DOI":"10.1007\/s40747-021-00494-8"},{"key":"e_1_2_14_24_2","doi-asserted-by":"publisher","DOI":"10.1186\/s13321-022-00635-2"},{"key":"e_1_2_14_25_2","doi-asserted-by":"publisher","DOI":"10.52041\/serj.v21i2.61"},{"key":"e_1_2_14_26_2","doi-asserted-by":"crossref","unstructured":"GrotovK. 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