{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,3]],"date-time":"2026-06-03T04:45:48Z","timestamp":1780461948585,"version":"3.54.1"},"reference-count":36,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2025,5,31]],"date-time":"2025-05-31T00:00:00Z","timestamp":1748649600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["MAKE"],"abstract":"<jats:p>Brain tumor classification poses significant challenges in medical imaging, largely due to the heterogeneity and structural complexity of tumors. With Magnetic Resonance Imaging (MRI) serving as a cornerstone for diagnosis, manual interpretation by radiologists is time-consuming and prone to inter-observer variability. Recent advances in deep learning, particularly through the application of Convolutional Neural Networks (CNNs), have transformed medical image analysis by enabling automated, high-accuracy feature extraction. Despite their promise, the performance of CNNs is highly contingent upon optimal hyperparameter tuning, a process that can be both computationally demanding and pivotal for model efficacy. In this study, we employ Simulated Annealing (SA), a probabilistic metaheuristic technique, to methodically optimize the hyperparameters of a CNN architecture designed specifically for classifying brain tumors from MRI scans. Our approach employs a direct representation of hyperparameters alongside an efficient perturbation strategy, facilitating a comprehensive exploration of the parameter space. Experimental evaluations conducted on an extensive MRI dataset (N = 7023 scans classified into glioma, meningioma, no tumor and pituitary) demonstrate that our SA-optimized CNN model achieves a validation accuracy of 98.15%, thereby affirming the potential of SA in enhancing the performance of deep learning systems in medical diagnostics. These findings underscore the critical role of advanced hyperparameter optimization techniques in improving diagnostic accuracy and robustness, ultimately contributing to the development of more reliable and efficient brain tumor classification systems in clinical settings.<\/jats:p>","DOI":"10.3390\/make7020050","type":"journal-article","created":{"date-parts":[[2025,6,2]],"date-time":"2025-06-02T10:21:49Z","timestamp":1748859709000},"page":"50","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Simulated Annealing-Based Hyperparameter Optimization of a Convolutional Neural Network for MRI Brain Tumor Classification"],"prefix":"10.3390","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-6616-0527","authenticated-orcid":false,"given":"Sofia","family":"El Amoury","sequence":"first","affiliation":[{"name":"Laboratory RI, Faculty of Sciences of Kenitra, Ibn Tofail University, Kenitra 14000, Morocco"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-1745-969X","authenticated-orcid":false,"given":"Youssef","family":"Smili","sequence":"additional","affiliation":[{"name":"ENIC Team, Faculty of Science and Technology of Settat, Hassan First University, Settat 26000, Morocco"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5647-303X","authenticated-orcid":false,"given":"Youssef","family":"Fakhri","sequence":"additional","affiliation":[{"name":"Laboratory RI, Faculty of Sciences of Kenitra, Ibn Tofail University, Kenitra 14000, Morocco"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2025,5,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Hossain, A., Islam, M.T., Abdul Rahim, S.K., Rahman, M.A., Rahman, T., Arshad, H., Khandakar, A., Ayari, M.A., and Chowdhury, M.E.H. (2023). A Lightweight Deep Learning Based Microwave Brain Image Network Model for Brain Tumor Classification Using Reconstructed Microwave Brain (RMB) Images. Biosensors, 13.","DOI":"10.3390\/bios13020238"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2248","DOI":"10.3390\/make6040111","article-title":"Empowering Brain Tumor Diagnosis through Explainable Deep Learning","volume":"6","author":"Li","year":"2024","journal-title":"Mach. Learn. Knowl. Extr."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Minarno, A.E., Hazmi Cokro Mandiri, M., Munarko, Y., and Hariyady, H. (2021). Convolutional Neural Network with Hyperparameter Tuning for Brain Tumor Classification. Kinet. Game Technol. Inf. Syst. Comput. Network Comput. Electron. Control, 6.","DOI":"10.22219\/kinetik.v6i2.1219"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"CNS72","DOI":"10.2217\/cns-2021-0003","article-title":"Meningioma: Not always a benign tumor. A review of advances in the treatment of meningiomas","volume":"10","author":"Maggio","year":"2021","journal-title":"CNS Oncol."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Grochans, S., Cybulska, A.M., Simi\u0144ska, D., Korbecki, J., Kojder, K., Chlubek, D., and Baranowska-Bosiacka, I. (2022). Epidemiology of Glioblastoma Multiforme\u2013Literature Review. Cancers, 14.","DOI":"10.3390\/cancers14102412"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"789","DOI":"10.1007\/s11604-023-01400-7","article-title":"Imaging of pituitary tumors: An update with the 5th WHO Classifications\u2014Part 1. Pituitary neuroendocrine tumor (PitNET)\/pituitary adenoma","volume":"41","author":"Tsukamoto","year":"2023","journal-title":"Jpn. J. Radiol."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"88","DOI":"10.11591\/ijaas.v10.i1.pp88-98","article-title":"Deep learning model for glioma, meningioma and pituitary classification","volume":"10","author":"Sadoon","year":"2021","journal-title":"Int. J. Adv. Appl. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Bauer, S., Wiest, R., Nolte, L.P., and Reyes, M. (2013). A survey of MRI-based medical image analysis for brain tumor studies. Phys. Med. Biol., 58.","DOI":"10.1088\/0031-9155\/58\/13\/R97"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"244","DOI":"10.1016\/j.patrec.2019.11.020","article-title":"Brain tumor segmentation and classification from magnetic resonance images: Review of selected methods from 2014 to 2019","volume":"131","author":"Tiwari","year":"2020","journal-title":"Pattern Recognit. Lett."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"436","DOI":"10.1038\/nature14539","article-title":"Deep learning","volume":"521","author":"LeCun","year":"2015","journal-title":"Nature"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1016\/j.media.2017.07.005","article-title":"A survey on deep learning in medical image analysis","volume":"42","author":"Litjens","year":"2017","journal-title":"Med. Image Anal."},{"key":"ref_12","first-page":"17","article-title":"Image Segmentation and Object Detection for Automobile using OpenCV and CNN","volume":"12","author":"Adaji","year":"2024","journal-title":"J. Netw. Inf. Secur."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1007\/s13198-022-01844-6","article-title":"Automation of surveillance systems using deep learning and facial recognition","volume":"14","author":"Singh","year":"2023","journal-title":"Int. J. Syst. Assur. Eng. Manag."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Gao, J., Bambrah, C.K., Parihar, N., Kshirsagar, S., Mallarapu, S., Yu, H., Wu, J., and Yang, Y. (2024). Analysis of Various Machine Learning Algorithms for Using Drone Images in Livestock Farms. Agriculture, 14.","DOI":"10.3390\/agriculture14040522"},{"key":"ref_15","unstructured":"Yu, T., and Zhu, H. (2020). Hyper-Parameter Optimization: A Review of Algorithms and Applications. arXiv."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Delahaye, D., Chaimatanan, S., and Mongeau, M. (2019). Simulated annealing: From basics to applications. Handbook of Metaheuristics, Springer.","DOI":"10.1007\/978-3-319-91086-4_1"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1016\/j.bbe.2018.10.004","article-title":"Magnetic resonance imaging-based brain tumor grades classification and grading via convolutional neural networks and genetic algorithms","volume":"39","author":"Anaraki","year":"2019","journal-title":"Biocybern. Biomed. Eng."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Amoury, S.E., Smili, Y., and Fakhri, Y. (2024, January 23\u201324). Optimization of Convolutional Neural Network Architecture by PSO Algorithm for MRI Brain Tumor Image Classification. Proceedings of the 2024 Sixth International Conference on Intelligent Computing in Data Sciences (ICDS), Marrakech, Morocco.","DOI":"10.1109\/ICDS62089.2024.10756503"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"El Amoury, S., Smili, Y., and Fakhri, Y. (2025). Design of an Optimal Convolutional Neural Network Architecture for MRI Brain Tumor Classification by Exploiting Particle Swarm Optimization. J. Imaging, 11.","DOI":"10.20944\/preprints202501.0040.v1"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"122159","DOI":"10.1016\/j.eswa.2023.122159","article-title":"Development of hybrid models based on deep learning and optimized machine learning algorithms for brain tumor Multi-Classification","volume":"238","author":"Celik","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_21","first-page":"576","article-title":"A Robust Hybrid Convolutional Network for Tumor Classification Using Brain MRI Image Datasets","volume":"15","author":"Bansal","year":"2024","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"G\u00f3mez-Guzm\u00e1n, M.A., Jim\u00e9nez-Berista\u00edn, L., Garc\u00eda-Guerrero, E.E., L\u00f3pez-Bonilla, O.R., Tamayo-Perez, U.J., Esqueda-Elizondo, J.J., Palomino-Vizcaino, K., and Inzunza-Gonz\u00e1lez, E. (2023). Classifying Brain Tumors on Magnetic Resonance Imaging by Using Convolutional Neural Networks. Electronics, 12.","DOI":"10.3390\/electronics12040955"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Ait Amou, M., Xia, K., Kamhi, S., and Mouhafid, M. (2022). A Novel MRI Diagnosis Method for Brain Tumor Classification Based on CNN and Bayesian Optimization. Healthcare, 10.","DOI":"10.3390\/healthcare10030494"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Singh, G., Chhabra, A., and Mittal, A. (2024, January 25\u201326). Evaluating Deep Learning Algorithms for MRI-Based Brain Tumor Classification. Proceedings of the 2024 International Conference on Emerging Innovations and Advanced Computing (INNOCOMP), Sonipat, India.","DOI":"10.1109\/INNOCOMP63224.2024.00076"},{"key":"ref_25","first-page":"1995","article-title":"Convolutional networks for images, speech, and time series","volume":"3361","author":"LeCun","year":"1995","journal-title":"Handb. Brain Theory Neural Netw."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Hao, W., Yizhou, W., Yaqin, L., and Zhili, S. (2020, January 18\u201320). The Role of Activation Function in CNN. Proceedings of the 2020 2nd International Conference on Information Technology and Computer Application (ITCA), Guangzhou, China.","DOI":"10.1109\/ITCA52113.2020.00096"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"187","DOI":"10.1007\/s40745-020-00253-5","article-title":"A Comprehensive Survey of Loss Functions in Machine Learning","volume":"9","author":"Wang","year":"2022","journal-title":"Ann. Data Sci."},{"key":"ref_28","unstructured":"Ian, G., Yoshua, B., and Aaron, C. (2016). Deep Learning, MIT Press."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"400","DOI":"10.1214\/aoms\/1177729586","article-title":"A Stochastic Approximation Method","volume":"22","author":"Robbins","year":"1951","journal-title":"Ann. Math. Stat."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/S0893-6080(98)00116-6","article-title":"On the momentum term in gradient descent learning algorithms","volume":"12","author":"Qian","year":"1999","journal-title":"Neural Netw."},{"key":"ref_31","unstructured":"Kingma, D.P., and Ba, J. (2017). Adam: A Method for Stochastic Optimization. arXiv."},{"key":"ref_32","first-page":"2146","article-title":"Neural networks for machine learning","volume":"264","author":"Hinton","year":"2012","journal-title":"Coursera Video Lect."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"3947","DOI":"10.1007\/s10462-019-09784-7","article-title":"A survey of regularization strategies for deep models","volume":"53","author":"Moradi","year":"2020","journal-title":"Artif. Intell. Rev."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Chopard, B., and Tomassini, M. (2018). Simulated Annealing. An Introduction to Metaheuristics for Optimization, Springer International Publishing.","DOI":"10.1007\/978-3-319-93073-2_4"},{"key":"ref_35","unstructured":"Nickparvar, M. (2025, May 09). Brain Tumor MRI Dataset. Available online: https:\/\/www.kaggle.com\/dsv\/2645886."},{"key":"ref_36","unstructured":"Cheng, J. (2025, May 09). Brain Tumor Dataset. Available online: https:\/\/figshare.com\/articles\/dataset\/brain_tumor_dataset\/1512427\/5."}],"container-title":["Machine Learning and Knowledge Extraction"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2504-4990\/7\/2\/50\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,9]],"date-time":"2025-10-09T17:44:49Z","timestamp":1760031889000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2504-4990\/7\/2\/50"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,31]]},"references-count":36,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2025,6]]}},"alternative-id":["make7020050"],"URL":"https:\/\/doi.org\/10.3390\/make7020050","relation":{},"ISSN":["2504-4990"],"issn-type":[{"value":"2504-4990","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,31]]}}}