{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,4]],"date-time":"2026-03-04T17:27:03Z","timestamp":1772645223579,"version":"3.50.1"},"reference-count":57,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2023,5,12]],"date-time":"2023-05-12T00:00:00Z","timestamp":1683849600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"MMSIT (Ministry of Science and ICT), Korea","award":["IITP-2023-RS-2022-00156287"],"award-info":[{"award-number":["IITP-2023-RS-2022-00156287"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>One of the most severe types of cancer caused by the uncontrollable proliferation of brain cells inside the skull is brain tumors. Hence, a fast and accurate tumor detection method is critical for the patient\u2019s health. Many automated artificial intelligence (AI) methods have recently been developed to diagnose tumors. These approaches, however, result in poor performance; hence, there is a need for an efficient technique to perform precise diagnoses. This paper suggests a novel approach for brain tumor detection via an ensemble of deep and hand-crafted feature vectors (FV). The novel FV is an ensemble of hand-crafted features based on the GLCM (gray level co-occurrence matrix) and in-depth features based on VGG16. The novel FV contains robust features compared to independent vectors, which improve the suggested method\u2019s discriminating capabilities. The proposed FV is then classified using SVM or support vector machines and the k-nearest neighbor classifier (KNN). The framework achieved the highest accuracy of 99% on the ensemble FV. The results indicate the reliability and efficacy of the proposed methodology; hence, radiologists can use it to detect brain tumors through MRI (magnetic resonance imaging). The results show the robustness of the proposed method and can be deployed in the real environment to detect brain tumors from MRI images accurately. In addition, the performance of our model was validated via cross-tabulated data.<\/jats:p>","DOI":"10.3390\/s23104693","type":"journal-article","created":{"date-parts":[[2023,5,12]],"date-time":"2023-05-12T09:56:18Z","timestamp":1683885378000},"page":"4693","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":41,"title":["A Novel Approach for Brain Tumor Classification Using an Ensemble of Deep and Hand-Crafted Features"],"prefix":"10.3390","volume":"23","author":[{"given":"Hareem","family":"Kibriya","sequence":"first","affiliation":[{"name":"Department of Computer Sciences, University of Engineering and Technology, Taxila 47050, Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3143-689X","authenticated-orcid":false,"given":"Rashid","family":"Amin","sequence":"additional","affiliation":[{"name":"Department of Computer Sciences, University of Chakwal, Chakwal 48800, Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2294-3969","authenticated-orcid":false,"given":"Jinsul","family":"Kim","sequence":"additional","affiliation":[{"name":"School of Electronics and Computer Engineering, Chonnam National University, 300 Yongbong-dong, Buk-gu, Gwangju 500757, Republic of Korea"}]},{"given":"Marriam","family":"Nawaz","sequence":"additional","affiliation":[{"name":"Department of Software Engineering, University of Engineering and Technology, Taxila 47050, Pakistan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5833-3914","authenticated-orcid":false,"given":"Rahma","family":"Gantassi","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, Chonnam National University, Gwangju 61186, Republic of Korea"}]}],"member":"1968","published-online":{"date-parts":[[2023,5,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1002\/ima.22255","article-title":"Development of computer-aided approach for brain tumor detection using random forest classifier","volume":"28","author":"Anitha","year":"2017","journal-title":"Int. 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