{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,15]],"date-time":"2026-09-15T10:56:44Z","timestamp":1789469804657,"version":"build-2803163510"},"reference-count":38,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2023,1,20]],"date-time":"2023-01-20T00:00:00Z","timestamp":1674172800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JPM"],"abstract":"<jats:p>The field of medical image processing plays a significant role in brain tumor classification. The survival rate of patients can be increased by diagnosing the tumor at an early stage. Several automatic systems have been developed to perform the tumor recognition process. However, the existing systems could be more efficient in identifying the exact tumor region and hidden edge details with minimum computation complexity. The Harris Hawks optimized convolution network (HHOCNN) is used in this work to resolve these issues. The brain magnetic resonance (MR) images are pre-processed, and the noisy pixels are eliminated to minimize the false tumor recognition rate. Then, the candidate region process is applied to identify the tumor region. The candidate region method investigates the boundary regions with the help of the line segments concept, which reduces the loss of hidden edge details. Various features are extracted from the segmented region, which is classified by applying a convolutional neural network (CNN). The CNN computes the exact region of the tumor with fault tolerance. The proposed HHOCNN system was implemented using MATLAB, and performance was evaluated using pixel accuracy, error rate, accuracy, specificity, and sensitivity metrics. The nature-inspired Harris Hawks optimization algorithm minimizes the misclassification error rate and improves the overall tumor recognition accuracy to 98% achieved on the Kaggle dataset.<\/jats:p>","DOI":"10.3390\/jpm13020181","type":"journal-article","created":{"date-parts":[[2023,1,20]],"date-time":"2023-01-20T02:35:55Z","timestamp":1674182155000},"page":"181","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":135,"title":["Brain Tumor Classification Using Meta-Heuristic Optimized Convolutional Neural Networks"],"prefix":"10.3390","volume":"13","author":[{"given":"Sarah Zuhair","family":"Kurdi","sequence":"first","affiliation":[{"name":"Medical College, Kufa University, Al.Najaf Teaching Hospital M.B.ch.B\/F.I.C.M Neurosurgery, Baghdad 54001, Iraq"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7963-0918","authenticated-orcid":false,"given":"Mohammed Hasan","family":"Ali","sequence":"additional","affiliation":[{"name":"Computer Techniques Engineering Department, Faculty of Information Technology, Imam Ja\u2019afar Al-Sadiq University, Baghdad 10021, Iraq"},{"name":"College of Computer Science and Mathematics, University of Kufa, Najaf 540011, Iraq"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mustafa Musa","family":"Jaber","sequence":"additional","affiliation":[{"name":"Department of Medical Instruments Engineering Techniques, Dijlah University College, Baghdad 00964, Iraq"},{"name":"Department of Medical Instruments Engineering Techniques, Al-Turath University College, Baghdad 10021, Iraq"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3138-3801","authenticated-orcid":false,"given":"Tanzila","family":"Saba","sequence":"additional","affiliation":[{"name":"Artificial Intelligence & Data Analytics Lab, CCIS Prince Sultan University, Riyadh 11586, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0101-0329","authenticated-orcid":false,"given":"Amjad","family":"Rehman","sequence":"additional","affiliation":[{"name":"Artificial Intelligence & Data Analytics Lab, CCIS Prince Sultan University, Riyadh 11586, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9990-1084","authenticated-orcid":false,"given":"Robertas","family":"Dama\u0161evi\u010dius","sequence":"additional","affiliation":[{"name":"Faculty of Applied Mathematics, Silesian University of Technology, 44-100 Gliwice, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Nazir, M., Khan, M.A., Saba, T., and Rehman, A. 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