{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,15]],"date-time":"2026-07-15T08:36:27Z","timestamp":1784104587099,"version":"3.55.0"},"reference-count":39,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,1,4]],"date-time":"2022-01-04T00:00:00Z","timestamp":1641254400000},"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>With the advancement in technology, machine learning can be applied to diagnose the mass\/tumor in the brain using magnetic resonance imaging (MRI). This work proposes a novel developed transfer deep-learning model for the early diagnosis of brain tumors into their subclasses, such as pituitary, meningioma, and glioma. First, various layers of isolated convolutional-neural-network (CNN) models are built from scratch to check their performances for brain MRI images. Then, the 22-layer, binary-classification (tumor or no tumor) isolated-CNN model is re-utilized to re-adjust the neurons\u2019 weights for classifying brain MRI images into tumor subclasses using the transfer-learning concept. As a result, the developed transfer-learned model has a high accuracy of 95.75% for the MRI images of the same MRI machine. Furthermore, the developed transfer-learned model has also been tested using the brain MRI images of another machine to validate its adaptability, general capability, and reliability for real-time application in the future. The results showed that the proposed model has a high accuracy of 96.89% for an unseen brain MRI dataset. Thus, the proposed deep-learning framework can help doctors and radiologists diagnose brain tumors early.<\/jats:p>","DOI":"10.3390\/s22010372","type":"journal-article","created":{"date-parts":[[2022,1,9]],"date-time":"2022-01-09T23:08:26Z","timestamp":1641769706000},"page":"372","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":216,"title":["Brain Tumor\/Mass Classification Framework Using Magnetic-Resonance-Imaging-Based Isolated and Developed Transfer Deep-Learning Model"],"prefix":"10.3390","volume":"22","author":[{"given":"Muhannad Faleh","family":"Alanazi","sequence":"first","affiliation":[{"name":"Radiology, Department of Internal Medicine, College of Medicine, Jouf University, Sakaka 72388, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7326-1813","authenticated-orcid":false,"given":"Muhammad Umair","family":"Ali","sequence":"additional","affiliation":[{"name":"Department of Unmanned Vehicle Engineering, Sejong University, Seoul 05006, Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5300-189X","authenticated-orcid":false,"given":"Shaik Javeed","family":"Hussain","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronics, Global College of Engineering and Technology, Muscat 112, Oman"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0716-3932","authenticated-orcid":false,"given":"Amad","family":"Zafar","sequence":"additional","affiliation":[{"name":"Department of Electrical Engineering, The Ibadat International University, Islamabad 54590, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5459-5631","authenticated-orcid":false,"given":"Mohammed","family":"Mohatram","sequence":"additional","affiliation":[{"name":"Department of Electrical and Electronics, Global College of Engineering and Technology, Muscat 112, Oman"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4161-6875","authenticated-orcid":false,"given":"Muhammad","family":"Irfan","sequence":"additional","affiliation":[{"name":"Electrical Engineering Department, College of Engineering, Najran University, Najran 61441, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Raed","family":"AlRuwaili","sequence":"additional","affiliation":[{"name":"Radiology, Department of Internal Medicine, College of Medicine, Jouf University, Sakaka 72388, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mubarak","family":"Alruwaili","sequence":"additional","affiliation":[{"name":"Radiology, Department of Internal Medicine, College of Medicine, Jouf University, Sakaka 72388, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Naif H.","family":"Ali","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine, Medical College, Najran University, Najran 61441, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anas Mohammad","family":"Albarrak","sequence":"additional","affiliation":[{"name":"Department of Internal Medicine, College of Medicine, Prince Sattam Bin Abdulaziz University, Alkharj 16278, Saudi Arabia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"803","DOI":"10.1007\/s00401-016-1545-1","article-title":"The 2016 world health organization classification of tumors of the central nervous system: A summary","volume":"131","author":"Louis","year":"2016","journal-title":"Acta Neuropathol."},{"key":"ref_2","unstructured":"World Health Organization (2021, September 09). Cancer. Available online: https:\/\/www.who.int\/news-room\/fact-sheets\/detail\/cancer."},{"key":"ref_3","unstructured":"American Cancer Society (2021, September 09). Available online: www.cancer.org\/cancer.html."},{"key":"ref_4","unstructured":"(2021, September 09). Brain Tumor: Diagnosis. Available online: https:\/\/www.cancer.net\/cancer-types\/brain-tumor\/diagnosis."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Tandel, G.S., Biswas, M., Kakde, O.G., Tiwari, A., Suri, H.S., Turk, M., Laird, J.R., Asare, C.K., Ankrah, A.A., and Khanna, N.N. (2019). A Review on a deep learning perspective in brain cancer classification. Cancers, 11.","DOI":"10.3390\/cancers11010111"},{"key":"ref_6","first-page":"70","article-title":"Brain Cancer: Implication to disease, therapeutic strategies and tumor targeted drug delivery approaches","volume":"13","author":"Viral","year":"2018","journal-title":"Recent Pat. Anti-Cancer Drug Discov."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1109\/TITB.2011.2104376","article-title":"Efficacy of texture, shape, and intensity feature fusion for posterior-fossa tumor segmentation in MRI","volume":"15","author":"Ahmed","year":"2011","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"E1","DOI":"10.3171\/foc.2006.20.4.E1","article-title":"Trends in brain cancer incidence and survival in the United States: Surveillance, epidemiology, and end results program, 1973 to 2001","volume":"20","author":"Deorah","year":"2006","journal-title":"Neurosurg. Focus FOC"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Bad\u017ea, M.M., and Barjaktarovi\u0107, M.\u010c. (2020). Classification of brain tumors from MRI images using a convolutional neural Network. Appl. Sci., 10.","DOI":"10.3390\/app10061999"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"1240","DOI":"10.1109\/TMI.2016.2538465","article-title":"Brain tumor segmentation using convolutional neural networks in MRI images","volume":"35","author":"Pereira","year":"2016","journal-title":"IEEE Trans. Med. Imaging"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"198","DOI":"10.1016\/j.compmedimag.2007.02.002","article-title":"Computer-aided diagnosis in medical imaging: Historical review, current status and future potential","volume":"31","author":"Doi","year":"2007","journal-title":"Comput. Med. Imaging Graph."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Munir, K., Elahi, H., Ayub, A., Frezza, F., and Rizzi, A. (2019). cancer diagnosis using deep learning: A bibliographic review. Cancers, 11.","DOI":"10.3390\/cancers11091235"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"247","DOI":"10.1016\/j.mri.2019.05.043","article-title":"A review on brain tumor segmentation of MRI images","volume":"61","author":"Wadhwa","year":"2019","journal-title":"Magn. Reson. Imaging"},{"key":"ref_14","first-page":"1686","article-title":"SVM classification an approach on detecting abnormality in brain MRI images","volume":"3","author":"Kumari","year":"2013","journal-title":"Int. J. Eng. Res. Appl."},{"key":"ref_15","first-page":"243","article-title":"Classification of abnormalities in brain MRI images using GLCM, PCA and SVM","volume":"1","author":"Singh","year":"2012","journal-title":"Int. J. Eng. Adv. Technol."},{"key":"ref_16","unstructured":"Bosch, A., Munoz, X., Oliver, A., and Marti, J. (2006, January 17\u201322). Modeling and classifying breast tissue density in mammograms. Proceedings of the 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR06), New York, NY, USA."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Cheng, J., Yang, W., Huang, M., Huang, W., Jiang, J., Zhou, Y., Yang, R., Zhao, J., Feng, Y., and Feng, Q. (2016). Retrieval of brain tumors by adaptive spatial pooling and fisher vector representation. PLoS ONE, 11.","DOI":"10.1371\/journal.pone.0157112"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"144","DOI":"10.1016\/j.bspc.2018.10.010","article-title":"A hybrid feature extraction approach for brain MRI classification based on Bag-of-words","volume":"48","author":"Ayadi","year":"2019","journal-title":"Biomed. Signal. Processing Control"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"101940","DOI":"10.1016\/j.compmedimag.2021.101940","article-title":"Role of deep learning in brain tumor detection and classification (2015 to 2020): A review","volume":"91","author":"Nazir","year":"2021","journal-title":"Comput. Med. Imaging Graph."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Pereira, S., Meier, R., Alves, V., Reyes, M., and Silva, C.A. (2018). Automatic brain tumor grading from MRI data using convolutional neural networks and quality assessment. Understanding and Interpreting Machine Learning in Medical Image Computing Applications, Springer.","DOI":"10.1007\/978-3-030-02628-8_12"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Abiwinanda, N., Hanif, M., Hesaputra, S.T., Handayani, A., and Mengko, T.R. (2019). Brain tumor classification using convolutional neural network. World Congress on Medical Physics and Biomedical Engineering 2018, Springer.","DOI":"10.1007\/978-981-10-9035-6_33"},{"key":"ref_22","unstructured":"Jun, C. (2021, September 09). Brain Tumor Dataset. Available online: https:\/\/figshare.com\/articles\/dataset\/brain_tumor_dataset\/1512427."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1015","DOI":"10.1007\/s40998-021-00426-9","article-title":"Multi-Classification of brain tumor MRI images using deep convolutional neural network with fully optimized framework","volume":"45","author":"Irmak","year":"2021","journal-title":"Iran. J. Sci. Technol. Trans. Electr. Eng."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"103345","DOI":"10.1016\/j.compbiomed.2019.103345","article-title":"Brain tumor classification using deep CNN features via transfer learning","volume":"111","author":"Deepak","year":"2019","journal-title":"Comput. Biol. Med."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"109684","DOI":"10.1016\/j.mehy.2020.109684","article-title":"Detection of tumors on brain MRI images using the hybrid convolutional neural network architecture","volume":"139","author":"Yildirim","year":"2020","journal-title":"Med. Hypotheses"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Kang, J., Ullah, Z., and Gwak, J. (2021). MRI-based brain tumor classification using ensemble of deep features and machine learning classifiers. Sensors, 21.","DOI":"10.3390\/s21062222"},{"key":"ref_27","unstructured":"Hamada, A. (2021, September 09). Br35H Brain Tumor Detection 2020 Dataset. Available online: https:\/\/www.kaggle.com\/ahmedhamada0\/brain-tumor-detection\/metadata."},{"key":"ref_28","unstructured":"Bhuvaji, S., Kadam, A., Bhumkar, P., Dedge, S., and Kanchan, S. (2021, September 09). Brain Tumor Classification (MRI) Dataset. Available online: https:\/\/www.kaggle.com\/sartajbhuvaji\/brain-tumor-classification-mri."},{"key":"ref_29","unstructured":"Chollet, F. (2017). Deep Learning with Python, Simon and Schuster."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Tan, C., Sun, F., Kong, T., Zhang, W., Yang, C., and Liu, C. (2018, January 4\u20137). A survey on deep transfer learning. Proceedings of the International Conference on Artificial Neural Networks, Rhodes, Greece.","DOI":"10.1007\/978-3-030-01424-7_27"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1016\/j.solener.2020.01.055","article-title":"Automatic detection of photovoltaic module defects in infrared images with isolated and develop-model transfer deep learning","volume":"198","author":"Akram","year":"2020","journal-title":"Sol. Energy"},{"key":"ref_32","unstructured":"Rosebrock, A. (2021, September 09). Finding Extreme Points in Contours with Open CV. Available online: https:\/\/www.pyimagesearch.com\/2016\/04\/11\/finding-extreme-points-in-contours-with-opencv\/."},{"key":"ref_33","unstructured":"Sarkar, D., Bali, R., and Ghosh, T. (2018). Hands-On Transfer Learning with Python: Implement. Advanced Deep Learning and Neural Network Models Using TensorFlow and Keras, Packt Publishing Ltd."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"757","DOI":"10.1007\/s00034-019-01246-3","article-title":"A Deep learning-based framework for automatic brain tumors classification using transfer learning","volume":"39","author":"Rehman","year":"2020","journal-title":"Circ. Syst. Signal. Process."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"69215","DOI":"10.1109\/ACCESS.2019.2919122","article-title":"Multi-classification of brain tumor images using deep neural network","volume":"7","author":"Sultan","year":"2019","journal-title":"IEEE Access"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Kutlu, H., and Avc\u0131, E. (2019). A Novel method for classifying liver and brain tumors using convolutional neural networks, discrete wavelet transform and long short-term memory networks. Sensors, 19.","DOI":"10.3390\/s19091992"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1016\/j.compmedimag.2019.05.001","article-title":"Brain tumor classification for MR images using transfer learning and fine-tuning","volume":"75","author":"Swati","year":"2019","journal-title":"Comput. Med. Imaging Graph."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"174","DOI":"10.1016\/j.jocs.2018.12.003","article-title":"Multi-grade brain tumor classification using deep CNN with extensive data augmentation","volume":"30","author":"Sajjad","year":"2019","journal-title":"J. Comput. Sci."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Cheng, J., Huang, W., Cao, S., Yang, R., Yang, W., Yun, Z., Wang, Z., and Feng, Q. (2015). Correction: Enhanced performance of brain tumor classification via tumor region augmentation and partition. PLoS ONE, 10.","DOI":"10.1371\/journal.pone.0144479"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/1\/372\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T14:00:20Z","timestamp":1760364020000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/22\/1\/372"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,4]]},"references-count":39,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2022,1]]}},"alternative-id":["s22010372"],"URL":"https:\/\/doi.org\/10.3390\/s22010372","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,4]]}}}