{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T18:09:30Z","timestamp":1772042970871,"version":"3.50.1"},"reference-count":51,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2022,12,6]],"date-time":"2022-12-06T00:00:00Z","timestamp":1670284800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001321","name":"National Research Foundation of Korea (NRF)","doi-asserted-by":"publisher","award":["2022R1C1C2003637"],"award-info":[{"award-number":["2022R1C1C2003637"]}],"id":[{"id":"10.13039\/501100001321","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Life"],"abstract":"<jats:p>Brain tumors are among the deadliest diseases in the modern world. This study proposes an optimized machine-learning approach for the detection and identification of the type of brain tumor (glioma, meningioma, or pituitary tumor) in brain images recorded using magnetic resonance imaging (MRI). The Gaussian features of the image are extracted using speed-up robust features (SURF), whereas its non-linear features are obtained using KAZE, owing to their high performance against rotation, scaling, and noise problems. To retrieve local-level information, all brain MRI images are segmented into an 8 \u00d7 8 pixel grid. To enhance the accuracy and reduce the computational time, the variance-based k-means clustering and PSO-ReliefF algorithms are employed to eliminate the redundant features of the brain MRI images. Finally, the performance of the proposed hybrid optimized feature vector is evaluated using various machine learning classifiers. An accuracy of 96.30% is obtained with 169 features using a support vector machine (SVM). Furthermore, the computational time is also reduced to 1 min compared to the non-optimized features used for training of the SVM. The findings are also compared with previous research, demonstrating that the suggested approach might assist physicians and doctors in the timely detection of brain tumors.<\/jats:p>","DOI":"10.3390\/life12122036","type":"journal-article","created":{"date-parts":[[2022,12,7]],"date-time":"2022-12-07T02:18:48Z","timestamp":1670379528000},"page":"2036","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["A Robust Computer-Aided Automated Brain Tumor Diagnosis Approach Using PSO-ReliefF Optimized Gaussian and Non-Linear Feature Space"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7326-1813","authenticated-orcid":false,"given":"Muhammad Umair","family":"Ali","sequence":"first","affiliation":[{"name":"Department of Unmanned Vehicle Engineering, Sejong University, Seoul 05006, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Karam Dad","family":"Kallu","sequence":"additional","affiliation":[{"name":"Department of Robotics & Artificial Intelligence (R&AI), School of Mechanical and Manufacturing Engineering (SMME), National University of Sciences and Technology (NUST) H\u221212, Islamabad 44000, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haris","family":"Masood","sequence":"additional","affiliation":[{"name":"Electrical Engineering Department, Wah Engineering College, University of Wah, Wah Cantt 47040, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5300-189X","authenticated-orcid":false,"given":"Shaik Javeed","family":"Hussain","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronics, Global College of Engineering and Technology, Muscat 112, Oman"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Safee","family":"Ullah","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering HITEC University, Taxila 47080, Pakistan"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6334-8176","authenticated-orcid":false,"given":"Jong Hyuk","family":"Byun","sequence":"additional","affiliation":[{"name":"Department of Mathematics, College of Natural Sciences, Pusan National University, Busan 46241, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0716-3932","authenticated-orcid":false,"given":"Amad","family":"Zafar","sequence":"additional","affiliation":[{"name":"Department of Intelligent Mechatronics Engineering, Sejong University, Seoul 05006, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6626-316X","authenticated-orcid":false,"given":"Kawang Su","family":"Kim","sequence":"additional","affiliation":[{"name":"Department of Scientific computing, Pukyong National University, Busan 48513, Republic of Korea"},{"name":"Interdisciplinary Biology Laboratory (iBLab), Division of Biological Science, Graduate School of Science, Nagoya University, Nagoya 464-8602, Japan"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1049\/iet-cvi.2014.0193","article-title":"Brain tumour classification using two-tier classifier with adaptive segmentation technique","volume":"10","author":"Anitha","year":"2016","journal-title":"IET Comput. 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