{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,11]],"date-time":"2026-02-11T02:26:02Z","timestamp":1770776762634,"version":"3.50.0"},"reference-count":43,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,12,27]],"date-time":"2022-12-27T00:00:00Z","timestamp":1672099200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Brain abnormality causes severe human problems, and thorough screening is necessary to identify the disease. In clinics, bio-image-supported brain abnormality screening is employed mainly because of its investigative accuracy compared with bio-signal (EEG)-based practice. This research aims to develop a reliable disease screening framework for the automatic identification of schizophrenia (SCZ) conditions from brain MRI slices. This scheme consists following phases: (i) MRI slices collection and pre-processing, (ii) implementation of VGG16 to extract deep features (DF), (iii) collection of handcrafted features (HF), (iv) mayfly algorithm-supported optimal feature selection, (v) serial feature concatenation, and (vi) binary classifier execution and validation. The performance of the proposed scheme was independently tested with DF, HF, and concatenated features (DF+HF), and the achieved outcome of this study verifies that the schizophrenia screening accuracy with DF+HF is superior compared with other methods. During this work, 40 patients\u2019 brain MRI images (20 controlled and 20 SCZ class) were considered for the investigation, and the following accuracies were achieved: DF provided &gt;91%, HF obtained &gt;85%, and DF+HF achieved &gt;95%. Therefore, this framework is clinically significant, and in the future, it can be used to inspect actual patients\u2019 brain MRI slices.<\/jats:p>","DOI":"10.3390\/s23010280","type":"journal-article","created":{"date-parts":[[2022,12,28]],"date-time":"2022-12-28T05:31:54Z","timestamp":1672205514000},"page":"280","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Framework to Detect Schizophrenia in Brain MRI Slices with Mayfly Algorithm-Selected Deep and Handcrafted Features"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7184-6084","authenticated-orcid":false,"given":"K. Suresh","family":"Manic","sequence":"first","affiliation":[{"name":"National University of Science and Technology, Muscat P.O. Box 112, Oman"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3897-4460","authenticated-orcid":false,"given":"Venkatesan","family":"Rajinikanth","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Division of Research and Innovation, Saveetha School of Engineering, Saveetha Institute of Medical and Technical Sciences, Chennai 602105, India"}]},{"given":"Ali Saud","family":"Al-Bimani","sequence":"additional","affiliation":[{"name":"National University of Science and Technology, Muscat P.O. Box 112, Oman"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8862-3960","authenticated-orcid":false,"given":"David","family":"Taniar","sequence":"additional","affiliation":[{"name":"Faculty of Information Technology, Monash University, Wellington Rd, Clayton, VIC 3800, Australia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1939-4842","authenticated-orcid":false,"given":"Seifedine","family":"Kadry","sequence":"additional","affiliation":[{"name":"Department of Applied Data Science, Noroff University College, 4612 Kristiansand, Norway"},{"name":"Artificial Intelligence Research Center (AIRC), Ajman University, Ajman P.O. Box 346, United Arab Emirates"},{"name":"Department of Electrical and Computer Engineering, Lebanese American University, Byblos P.O. Box 36, Lebanon"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"She, Q., Chen, K., Luo, Z., Nguyen, T., Potter, T., and Zhang, Y. (2020). 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