{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T19:16:17Z","timestamp":1777058177126,"version":"3.51.4"},"reference-count":0,"publisher":"IBERAMIA: Sociedad Iberoamericana de Inteligencia Artificial","issue":"75","license":[{"start":{"date-parts":[[2024,12,4]],"date-time":"2024-12-04T00:00:00Z","timestamp":1733270400000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["ia"],"abstract":"<jats:p>This research proposes an effective and reliable deep learning method for detecting brain abnormalities via magnetic resonance imaging (MRI). The technique consists of two primary stages: first, a binary classifier that divides pictures into \"Brain\" and \"Non-Brain\" categories; second, multi-class classifiers that explicitly recognise categories such pituitary adenomas, gliomas, and meningiomas. The labelled and preprocessed data were taken from a collection of 7,753 pictures provided by Qhills Technologies Pvt. Ltd. Additional data from the Brain Tumour MRI collection was also incorporated to improve the model's generalisation skills. VGG-16 outperforms the other machine learning models, with an accuracy rate of 96.4%, when compared to ANN, CNN, VGG-16, and AlexNet. A thorough model evaluation and hyperparameter tweaking process was conducted using the accuracy, precision, recall F1-score. The findings of this study point to the potential of deep learning techniques in identifying brain disorders fast and precisely, opening the door to more precise diagnosis in clinical settings.<\/jats:p>","DOI":"10.4114\/intartif.vol28iss75pp81-100","type":"journal-article","created":{"date-parts":[[2024,12,4]],"date-time":"2024-12-04T10:22:16Z","timestamp":1733307736000},"page":"81-100","source":"Crossref","is-referenced-by-count":4,"title":["An Efficient Deep Learning Technique for Brain Abnormality Detection Using MRI"],"prefix":"10.4114","volume":"28","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8975-8114","authenticated-orcid":false,"given":"Shilpa","family":"Mahajan","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anuradha","family":"Dhull","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Aryan","family":"Dahiya","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"2598","published-online":{"date-parts":[[2024,12,4]]},"container-title":["Inteligencia Artificial"],"original-title":[],"link":[{"URL":"https:\/\/journal.iberamia.org\/index.php\/intartif\/article\/download\/1456\/239","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journal.iberamia.org\/index.php\/intartif\/article\/download\/1456\/239","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,4]],"date-time":"2024-12-04T10:22:17Z","timestamp":1733307737000},"score":1,"resource":{"primary":{"URL":"https:\/\/journal.iberamia.org\/index.php\/intartif\/article\/view\/1456"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,4]]},"references-count":0,"journal-issue":{"issue":"75","published-online":{"date-parts":[[2024,10,19]]}},"URL":"https:\/\/doi.org\/10.4114\/intartif.vol28iss75pp81-100","relation":{},"ISSN":["1988-3064","1137-3601"],"issn-type":[{"value":"1988-3064","type":"electronic"},{"value":"1137-3601","type":"print"}],"subject":[],"published":{"date-parts":[[2024,12,4]]}}}