{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T16:52:24Z","timestamp":1786121544030,"version":"build-2736575974"},"reference-count":18,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T00:00:00Z","timestamp":1786060800000},"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. Neurosci."],"abstract":"<jats:sec>\n                    <jats:title>Introduction<\/jats:title>\n                    <jats:p>Brain MRI image segmentation is essential for the accurate diagnosis and treatment of neurological disorders, including brain tumors, Alzheimer\u2019s disease, and multiple sclerosis. Artificial intelligence (AI), particularly deep learning, has emerged as an effective approach for improving the precision and efficiency of medical image segmentation while reducing manual effort.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>This study utilized a publicly available Kaggle brain MRI dataset containing labeled images for supervised learning. A Convolutional Neural Network (CNN)-based framework was developed for automatic brain MRI segmentation. The methodology incorporated preprocessing techniques, including noise removal, normalization, and data augmentation, to improve image quality and model performance. The proposed model was evaluated using Accuracy, Dice Score, and Intersection over Union (IoU).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Experimental results demonstrated that the proposed AI-based segmentation framework achieved high segmentation accuracy and effectively distinguished normal brain tissue from abnormal regions. The model outperformed conventional image-processing methods by providing improved segmentation precision, reducing manual intervention, and enhancing the reliability of medical image analysis.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>The findings demonstrate the potential of AI-based deep learning techniques for automated brain MRI segmentation in clinical applications. The proposed framework can support clinicians by improving diagnostic accuracy and reducing processing time. Future work will focus on implementing more advanced deep learning architectures and expanding the dataset to further improve segmentation performance and clinical applicability.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fnins.2026.1894751","type":"journal-article","created":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T05:28:41Z","timestamp":1786080521000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Developing artificial intelligence-based techniques for brain MRI image segmentation"],"prefix":"10.3389","volume":"20","author":[{"given":"Salliah","family":"Shafi","sequence":"first","affiliation":[{"name":"School of Computational Science, GNA University, Sri Hargobindgarh","place":["Phagwara, Punjab, India"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Gufran Ahmad","family":"Ansari","sequence":"additional","affiliation":[{"name":"College of Computer and Information Sciences Imam Mohammad Ibn Saud Islamic University (IMSIU)","place":["Riyadh, Saudi Arabia"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,8,7]]},"reference":[{"key":"ref1","first-page":"120105","article-title":"Improving segmentation accuracy with data augmentation in MRI","volume":"235","author":"Ahmed","year":"2024","journal-title":"Expert Syst. 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Roentgenol."},{"key":"ref11","doi-asserted-by":"publisher","first-page":"102196","DOI":"10.1016\/j.artmed.2021.102196","article-title":"Fine-tuning deep learning model parameters for improved super-resolution of dynamic MRI with prior-knowledge","volume":"121","author":"Sarasaen","year":"2021","journal-title":"Artif. Intell. Med."},{"key":"ref12","first-page":"75","volume-title":"Cognitive Security and Predictive Health Monitoring for Rural Development","author":"Shafi","year":""},{"key":"ref13","doi-asserted-by":"publisher","first-page":"860","DOI":"10.1007\/s42979-025-04392-5","article-title":"Cuckoo-inspired algorithms for selecting features in the prediction of diabetes using machine learning models","volume":"6","author":"Shafi","year":"2025","journal-title":"SN Comput. 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