{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,17]],"date-time":"2026-10-17T16:37:10Z","timestamp":1792255030977,"version":"4.4.0"},"reference-count":130,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2025,4,26]],"date-time":"2025-04-26T00:00:00Z","timestamp":1745625600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Italian Ministry for Universities and Research (MUR) under the grant Future Artificial Intelligence Research\u2014FAIR CUP B53C220036 30006","award":["PE0000013"],"award-info":[{"award-number":["PE0000013"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>A brain tumor is the result of abnormal growth of cells in the central nervous system (CNS), widely considered as a complex and diverse clinical entity that is difficult to diagnose and cure. In this study, we focus on current advances in medical imaging, particularly magnetic resonance imaging (MRI), and how machine learning (ML) and deep learning (DL) algorithms might be combined with clinical assessments to improve brain tumor diagnosis. Due to its superior contrast resolution and safety compared to other imaging methods, MRI is highlighted as the preferred imaging modality for brain tumors. The challenges related to brain tumor analysis in different processes including detection, segmentation, classification, and survival prediction are addressed along with how ML\/DL approaches significantly improve these steps. We systematically analyzed 107 studies (2018\u20132024) employing ML, DL, and hybrid models across publicly available datasets such as BraTS, TCIA, and Figshare. In the light of recent developments in brain tumor analysis, many algorithms have been proposed to accurately obtain ontological characteristics of tumors, enhancing diagnostic precision and personalized therapeutic strategies.<\/jats:p>","DOI":"10.3390\/s25092746","type":"journal-article","created":{"date-parts":[[2025,4,28]],"date-time":"2025-04-28T06:23:32Z","timestamp":1745821412000},"page":"2746","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":55,"title":["Advanced Deep Learning and Machine Learning Techniques for MRI Brain Tumor Analysis: A Review"],"prefix":"10.3390","volume":"25","author":[{"given":"Rim","family":"Missaoui","sequence":"first","affiliation":[{"name":"Laboratory of Micro-Optoelectronics and Nanostructures (LMON), University of Monastir, Avenue of the Environment, Monastir 5019, Tunisia"},{"name":"National High School of Engineering of Tunis (ENSIT), 5 Rue Taha Hussein\u2013Montfleury, Tunis 1008, Tunisia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1299-1804","authenticated-orcid":false,"given":"Wided","family":"Hechkel","sequence":"additional","affiliation":[{"name":"Laboratory of Micro-Optoelectronics and Nanostructures (LMON), University of Monastir, Avenue of the Environment, Monastir 5019, Tunisia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8977-9652","authenticated-orcid":false,"given":"Wajdi","family":"Saadaoui","sequence":"additional","affiliation":[{"name":"LRMAN Laboratory, Higher Institute of Applied Sciences and Technology of Kasserine (ISSAT), Kasserine 1200, Tunisia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1623-6830","authenticated-orcid":false,"given":"Abdelhamid","family":"Helali","sequence":"additional","affiliation":[{"name":"Laboratory of Micro-Optoelectronics and Nanostructures (LMON), University of Monastir, Avenue of the Environment, Monastir 5019, Tunisia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5636-6130","authenticated-orcid":false,"given":"Marco","family":"Leo","sequence":"additional","affiliation":[{"name":"Institute of Applied Sciences and Intelligent Systems, National Research Council of Italy, 73100 Lecce, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,4,26]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"6229","DOI":"10.1016\/j.jksuci.2021.05.008","article-title":"Efficient and low complex architecture for detection and classification of Brain Tumor using RCNN with Two Channel CNN","volume":"34","author":"Kesav","year":"2022","journal-title":"J. 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