{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,29]],"date-time":"2026-09-29T17:25:01Z","timestamp":1790702701681,"version":"4.1.0"},"reference-count":78,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2023,3,23]],"date-time":"2023-03-23T00:00:00Z","timestamp":1679529600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Creating machines that behave and work in a way similar to humans is the objective of artificial intelligence (AI). In addition to pattern recognition, planning, and problem-solving, computer activities with artificial intelligence include other activities. A group of algorithms called \u201cdeep learning\u201d is used in machine learning. With the aid of magnetic resonance imaging (MRI), deep learning is utilized to create models for the detection and categorization of brain tumors. This allows for the quick and simple identification of brain tumors. Brain disorders are mostly the result of aberrant brain cell proliferation, which can harm the structure of the brain and ultimately result in malignant brain cancer. The early identification of brain tumors and the subsequent appropriate treatment may lower the death rate. In this study, we suggest a convolutional neural network (CNN) architecture for the efficient identification of brain tumors using MR images. This paper also discusses various models such as ResNet-50, VGG16, and Inception V3 and conducts a comparison between the proposed architecture and these models. To analyze the performance of the models, we considered different metrics such as the accuracy, recall, loss, and area under the curve (AUC). As a result of analyzing different models with our proposed model using these metrics, we concluded that the proposed model performed better than the others. Using a dataset of 3264 MR images, we found that the CNN model had an accuracy of 93.3%, an AUC of 98.43%, a recall of 91.19%, and a loss of 0.25. We may infer that the proposed model is reliable for the early detection of a variety of brain tumors after comparing it to the other models.<\/jats:p>","DOI":"10.3390\/a16040176","type":"journal-article","created":{"date-parts":[[2023,3,24]],"date-time":"2023-03-24T02:34:54Z","timestamp":1679625294000},"page":"176","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":319,"title":["A Deep Analysis of Brain Tumor Detection from MR Images Using Deep Learning Networks"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6280-7035","authenticated-orcid":false,"given":"Md Ishtyaq","family":"Mahmud","sequence":"first","affiliation":[{"name":"College of Science and Engineering, Central Michigan University, Mount Pleasant, MI 48858, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muntasir","family":"Mamun","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of South Dakota, Vermillion, SD 57069, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6655-2065","authenticated-orcid":false,"given":"Ahmed","family":"Abdelgawad","sequence":"additional","affiliation":[{"name":"College of Science and Engineering, Central Michigan University, Mount Pleasant, MI 48858, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Qureshi, S.A., Raza, S.E.A., Hussain, L., Malibari, A.A., Nour, M.K., Rehman, A.U., Al-Wesabi, F.N., and Hilal, A.M. 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