{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,24]],"date-time":"2026-04-24T19:21:22Z","timestamp":1777058482246,"version":"3.51.4"},"reference-count":47,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2024,3,1]],"date-time":"2024-03-01T00:00:00Z","timestamp":1709251200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"The funder is me","award":["150000"],"award-info":[{"award-number":["150000"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computation"],"abstract":"<jats:p>Brain tumor diagnosis traditionally relies on the manual examination of magnetic resonance images (MRIs), a process that is prone to human error and is also time consuming. Recent advancements leverage machine learning models to categorize tumors, such as distinguishing between \u201cmalignant\u201d and \u201cbenign\u201d classes. This study focuses on the supervised machine learning task of classifying \u201cfirm\u201d and \u201csoft\u201d meningiomas, critical for determining optimal brain tumor treatment. The research aims to enhance meningioma firmness detection using state-of-the-art deep learning architectures. The study employs a YOLO architecture adapted for meningioma classification (Firm vs. Soft). This YOLO-based model serves as a machine learning component within a proposed CAD system. To improve model generalization and combat overfitting, transfer learning and data augmentation techniques are explored. Intra-model analysis is conducted for each of the five YOLO versions, optimizing parameters such as the optimizer, batch size, and learning rate based on sensitivity and training time. YOLOv3, YOLOv4, and YOLOv7 demonstrate exceptional sensitivity, reaching 100%. Comparative analysis against state-of-the-art models highlights their superiority. YOLOv7, utilizing the SGD optimizer, a batch size of 64, and a learning rate of 0.01, achieves outstanding overall performance with metrics including mean average precision (99.96%), precision (98.50%), specificity (97.95%), balanced accuracy (98.97%), and F1-score (99.24%). This research showcases the effectiveness of YOLO architectures in meningioma firmness detection, with YOLOv7 emerging as the optimal model. The study\u2019s findings underscore the significance of model selection and parameter optimization for achieving high sensitivity and robust overall performance in brain tumor classification.<\/jats:p>","DOI":"10.3390\/computation12030044","type":"journal-article","created":{"date-parts":[[2024,3,1]],"date-time":"2024-03-01T06:07:53Z","timestamp":1709273273000},"page":"44","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["A Deep Learning Approach for Brain Tumor Firmness Detection Based on Five Different YOLO Versions: YOLOv3\u2013YOLOv7"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-0086-0994","authenticated-orcid":false,"given":"Norah Fahd","family":"Alhussainan","sequence":"first","affiliation":[{"name":"Computer Engineering Department, College of Computer and Information Sciences, King Saud University, P.O. Box 51178, Riyadh 11543, Saudi Arabia"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6618-3845","authenticated-orcid":false,"given":"Belgacem","family":"Ben Youssef","sequence":"additional","affiliation":[{"name":"Computer Engineering Department, College of Computer and Information Sciences, King Saud University, P.O. Box 51178, Riyadh 11543, Saudi Arabia"}]},{"given":"Mohamed Maher","family":"Ben Ismail","sequence":"additional","affiliation":[{"name":"Computer Science Department, College of Computer and Information Sciences, King Saud University, P.O. Box 51178, Riyadh 11543, Saudi Arabia"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"209","DOI":"10.3322\/caac.21660","article-title":"Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries","volume":"71","author":"Sung","year":"2021","journal-title":"CA Cancer J. 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