{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T16:37:12Z","timestamp":1776357432135,"version":"3.51.2"},"reference-count":34,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T00:00:00Z","timestamp":1776297600000},"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. Neuroinform."],"abstract":"<jats:sec>\n                    <jats:title>Background<\/jats:title>\n                    <jats:p>Brain tumor diagnosis from magnetic resonance imaging (MRI) remains a challenging task due to the high variability in tumor appearance and the limitations of manual interpretation.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>To address these challenges, this paper proposes NeuroFusionNet, a deep learning framework for automated brain tumor classification from MRI. The framework integrates GAN-based synthetic image generation with transfer learning using a fine-tuned VGG16 backbone. Real and GAN-generated MRI images are passed through VGG16 to extract discriminative feature representations, which are then used for final classification. To adapt the model to domain-specific MRI characteristics while preserving pretrained knowledge, the last ten layers of VGG16 are fine-tuned and the remaining layers are kept frozen.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The effectiveness of NeuroFusionNet is validated on two publicly available brain MRI datasets. Experimental results demonstrate that the proposed learning framework achieves classification accuracies of 99.05 and 98.75% on the Brain Tumor MRI Dataset and the MRI with Bounding Boxes Dataset, respectively, consistently outperforming several state-of-the-art neural architectures, including VGG16, VGG19, MobileNetV2, DenseNet121, and NASNetLarge.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The results suggest that NeuroFusionNet is effective for the evaluated public MRI datasets; additional external validation is required.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.3389\/fninf.2026.1795354","type":"journal-article","created":{"date-parts":[[2026,4,16]],"date-time":"2026-04-16T15:32:36Z","timestamp":1776353556000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["A deep learning based NeuroFusionNet approach for automated brain tumor diagnosis from MRI"],"prefix":"10.3389","volume":"20","author":[{"given":"Omara","family":"Mustafa","sequence":"first","affiliation":[{"name":"Department of Radiology, Korean Medical Center","place":["Lusail, Qatar"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Salem","family":"Alhatamleh","sequence":"additional","affiliation":[{"name":"Computer Science Department, Faculty of Information Technology and Computer Sciences, Yarmouk University","place":["Irbid, Jordan"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hamad Yahia Abu","family":"Mhanna","sequence":"additional","affiliation":[{"name":"Department of Medical Imaging, Faculty of Allied Medical Sciences, Isra University","place":["Amman, Jordan"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abdallah","family":"Almahmoud","sequence":"additional","affiliation":[{"name":"Department of Allied Medical Sciences-Radiologic Technology, Faculty of Applied Medical Sciences, Jordan University of Science and Technology","place":["Irbid, Jordan"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rami","family":"Malkawi","sequence":"additional","affiliation":[{"name":"Department of Information Systems, Faculty of Information Technology and Computer Science, Yarmouk University","place":["Irbid, Jordan"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Majd","family":"Malkawi","sequence":"additional","affiliation":[{"name":"Faculty of Medicine, Jordan University of Science and Technology","place":["Irbid, Jordan"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abdel-Baset Bani","family":"Yaseen","sequence":"additional","affiliation":[{"name":"Department of Medical Imaging, Faculty of Applied Medical Sciences, The Hashemite University","place":["Zarqa, Jordan"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hanan Fawaz","family":"Akhdar","sequence":"additional","affiliation":[{"name":"Physics Department, College of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU)","place":["Riyadh, Saudi Arabia"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hatem","family":"Malkawi","sequence":"additional","affiliation":[{"name":"Faculty of Medicine, Jordan University of Science and Technology","place":["Irbid, Jordan"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fatimah","family":"Maashey","sequence":"additional","affiliation":[{"name":"Physics Department, College of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU)","place":["Riyadh, Saudi Arabia"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Latifah","family":"Alghulayqah","sequence":"additional","affiliation":[{"name":"Physics Department, College of Science, Imam Mohammad Ibn Saud Islamic University (IMSIU)","place":["Riyadh, Saudi Arabia"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Mohammad","family":"Amin","sequence":"additional","affiliation":[{"name":"Computer Science Department, Faculty of Information Technology and Computer Sciences, Yarmouk University","place":["Irbid, Jordan"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2026,4,16]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"209517","DOI":"10.1109\/access.2020.3038225","article-title":"Building footprint extraction from high resolution aerial images using generative adversarial network (GAN) architecture","volume":"8","author":"Abdollahi","year":"2020","journal-title":"IEEE Access"},{"key":"ref2","doi-asserted-by":"publisher","first-page":"118","DOI":"10.1186\/s12880-024-01285-6","article-title":"Refining neural network algorithms for accurate brain tumor classification in MRI imagery","volume":"24","author":"Alshuhail","year":"2024","journal-title":"BMC Med. 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