{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,18]],"date-time":"2026-08-18T04:38:44Z","timestamp":1787027924650,"version":"build-2736575974"},"reference-count":48,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2022,10,20]],"date-time":"2022-10-20T00:00:00Z","timestamp":1666224000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100004242","name":"Princess Nourah bint Abdulrahman University, Riyadh","doi-asserted-by":"publisher","award":["PNURSP2022R206"],"award-info":[{"award-number":["PNURSP2022R206"]}],"id":[{"id":"10.13039\/501100004242","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Diagnostics"],"abstract":"<jats:p>Brain tumors (BTs) are deadly diseases that can strike people of every age, all over the world. Every year, thousands of people die of brain tumors. Brain-related diagnoses require caution, and even the smallest error in diagnosis can have negative repercussions. Medical errors in brain tumor diagnosis are common and frequently result in higher patient mortality rates. Magnetic resonance imaging (MRI) is widely used for tumor evaluation and detection. However, MRI generates large amounts of data, making manual segmentation difficult and laborious work, limiting the use of accurate measurements in clinical practice. As a result, automated and dependable segmentation methods are required. Automatic segmentation and early detection of brain tumors are difficult tasks in computer vision due to their high spatial and structural variability. Therefore, early diagnosis or detection and treatment are critical. Various traditional Machine learning (ML) techniques have been used to detect various types of brain tumors. The main issue with these models is that the features were manually extracted. To address the aforementioned insightful issues, this paper presents a hybrid deep transfer learning (GN-AlexNet) model of BT tri-classification (pituitary, meningioma, and glioma). The proposed model combines GoogleNet architecture with the AlexNet model by removing the five layers of GoogleNet and adding ten layers of the AlexNet model, which extracts features and classifies them automatically. On the same CE-MRI dataset, the proposed model was compared to transfer learning techniques (VGG-16, AlexNet, SqeezNet, ResNet, and MobileNet-V2) and ML\/DL. The proposed model outperformed the current methods in terms of accuracy and sensitivity (accuracy of 99.51% and sensitivity of 98.90%).<\/jats:p>","DOI":"10.3390\/diagnostics12102541","type":"journal-article","created":{"date-parts":[[2022,10,20]],"date-time":"2022-10-20T20:35:55Z","timestamp":1666298155000},"page":"2541","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":54,"title":["Classification Framework for Medical Diagnosis of Brain Tumor with an Effective Hybrid Transfer Learning Model"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5957-1383","authenticated-orcid":false,"given":"Nagwan Abdel","family":"Samee","sequence":"first","affiliation":[{"name":"Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0608-6661","authenticated-orcid":false,"given":"Noha F.","family":"Mahmoud","sequence":"additional","affiliation":[{"name":"Rehabilitation Sciences Department, Health and Rehabilitation Sciences College, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5462-595X","authenticated-orcid":false,"given":"Ghada","family":"Atteia","sequence":"additional","affiliation":[{"name":"Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0307-1384","authenticated-orcid":false,"given":"Hanaa A.","family":"Abdallah","sequence":"additional","affiliation":[{"name":"Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Maali","family":"Alabdulhafith","sequence":"additional","affiliation":[{"name":"Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2612-0978","authenticated-orcid":false,"given":"Mehdhar S. A. M.","family":"Al-Gaashani","sequence":"additional","affiliation":[{"name":"College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shahab","family":"Ahmad","sequence":"additional","affiliation":[{"name":"School of Economics & Management, Chongqing University of Post and Telecommunication, Chongqing 400065, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1165-7812","authenticated-orcid":false,"given":"Mohammed Saleh Ali","family":"Muthanna","sequence":"additional","affiliation":[{"name":"Institute of Computer Technologies and Information Security, Southern Federal University, 347922 Taganrog, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,10,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"166","DOI":"10.3322\/caac.20069","article-title":"Exciting new advances in neuro-oncology: The avenue to a cure for malignant glioma","volume":"60","author":"Hadjipanayis","year":"2010","journal-title":"CA A Cancer J. 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