{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,19]],"date-time":"2026-06-19T12:13:45Z","timestamp":1781871225874,"version":"3.54.5"},"reference-count":50,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2022,2,24]],"date-time":"2022-02-24T00:00:00Z","timestamp":1645660800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Wroc\u0142aw University of Science and Technology","award":["K38W05D02"],"award-info":[{"award-number":["K38W05D02"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Usage of effective classification techniques on Magnetic Resonance Imaging (MRI) helps in the proper diagnosis of brain tumors. Previous studies have focused on the classification of normal (nontumorous) or abnormal (tumorous) brain MRIs using methods such as Support Vector Machine (SVM) and AlexNet. In this paper, deep learning architectures are used to classify brain MRI images into normal or abnormal. Gender and age are added as higher attributes for more accurate and meaningful classification. A deep learning Convolutional Neural Network (CNN)-based technique and a Deep Neural Network (DNN) are also proposed for effective classification. Other deep learning architectures such as LeNet, AlexNet, ResNet, and traditional approaches such as SVM are also implemented to analyze and compare the results. Age and gender biases are found to be more useful and play a key role in classification, and they can be considered essential factors in brain tumor analysis. It is also worth noting that, in most circumstances, the proposed technique outperforms both existing SVM and AlexNet. The overall accuracy obtained is 88% (LeNet Inspired Model) and 80% (CNN-DNN) compared to SVM (82%) and AlexNet (64%), with best accuracy of 100%, 92%, 92%, and 81%, respectively.<\/jats:p>","DOI":"10.3390\/s22051766","type":"journal-article","created":{"date-parts":[[2022,2,24]],"date-time":"2022-02-24T21:11:07Z","timestamp":1645737067000},"page":"1766","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":66,"title":["Brain Magnetic Resonance Imaging Classification Using Deep Learning Architectures with Gender and Age"],"prefix":"10.3390","volume":"22","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5493-8393","authenticated-orcid":false,"given":"Imayanmosha","family":"Wahlang","sequence":"first","affiliation":[{"name":"Department of Information Technology, North-Eastern Hill University, Shillong 793022, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3320-9965","authenticated-orcid":false,"given":"Arnab Kumar","family":"Maji","sequence":"additional","affiliation":[{"name":"Department of Information Technology, North-Eastern Hill University, Shillong 793022, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Goutam","family":"Saha","sequence":"additional","affiliation":[{"name":"Department of Information Technology, North-Eastern Hill University, Shillong 793022, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Prasun","family":"Chakrabarti","sequence":"additional","affiliation":[{"name":"Techno India NJR Institute of Technology, Udaipur 313003, India"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0983-2562","authenticated-orcid":false,"given":"Michal","family":"Jasinski","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering Fundamentals, Faculty of Electrical Engineering, Wroclaw University of Science and Technology, 50-370 Wroclaw, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2388-3710","authenticated-orcid":false,"given":"Zbigniew","family":"Leonowicz","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering Fundamentals, Faculty of Electrical Engineering, Wroclaw University of Science and Technology, 50-370 Wroclaw, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2433-3873","authenticated-orcid":false,"given":"Elzbieta","family":"Jasinska","sequence":"additional","affiliation":[{"name":"Department of Operations Research and Business Intelligence, Wroc\u0142aw University of Science and Technology, 50-370 Wroc\u0142aw, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,2,24]]},"reference":[{"key":"ref_1","unstructured":"(2018, July 20). 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