{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,27]],"date-time":"2026-05-27T20:40:40Z","timestamp":1779914440596,"version":"3.53.1"},"reference-count":31,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2024,8,16]],"date-time":"2024-08-16T00:00:00Z","timestamp":1723766400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Brain tumour disease develops due to abnormal cell proliferation. The early identification of brain tumours is vital for their effective treatment. Most currently available examination methods are laborious, require extensive manual instructions, and produce subpar findings. The EfficientNet-B0 architecture was used to diagnose brain tumours using magnetic resonance imaging (MRI). The fine-tuned EffeceintNet B0 model was proposed for the Internet of Medical Things (IoMT) environment. The fine-tuned EfficientNet-B0 architecture was employed to classify four different stages of brain tumours from the MRI images. The fine-tuned model showed 99% accuracy in the detection of four different classes of brain tumour detection (glioma, no tumour, meningioma, and pituitary). The proposed model performed very well in the detection of the pituitary class with a precision of 0.95, recall of 0.98, and F1 score of 0.96. The proposed model also performed very well in the detection of the no-tumour class with values of 0.99, 0.90, and 0.94 for precision, recall, and the F1 score, respectively. The precision, recall, and F1 scores for Glioma and Meningioma classes were also high. The proposed solution has several implications for enhancing clinical investigations of brain tumours.<\/jats:p>","DOI":"10.3390\/info15080489","type":"journal-article","created":{"date-parts":[[2024,8,16]],"date-time":"2024-08-16T06:23:20Z","timestamp":1723789400000},"page":"489","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":21,"title":["Enhancing Brain Tumour Multi-Classification Using Efficient-Net B0-Based Intelligent Diagnosis for Internet of Medical Things (IoMT) Applications"],"prefix":"10.3390","volume":"15","author":[{"given":"Amna","family":"Iqbal","sequence":"first","affiliation":[{"name":"Faculty of Computer Science & Information Technology, The Superior University, Lahore 54000, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Muhammad Arfan","family":"Jaffar","sequence":"additional","affiliation":[{"name":"Faculty of Computer Science & Information Technology, The Superior University, Lahore 54000, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1129-6006","authenticated-orcid":false,"given":"Rashid","family":"Jahangir","sequence":"additional","affiliation":[{"name":"Department of Computer Science, COMSATS University Islamabad, Vehari Campus, Vehari 61100, Pakistan"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/JSTQE.2019.2950795","article-title":"Polarization-sensitive optical coherence tomography for brain tumor characterization","volume":"25","author":"Li","year":"2019","journal-title":"IEEE J. Sel. Top. Quantum Electron."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"E168","DOI":"10.1093\/neuros\/nyy543","article-title":"Congress of neurological surgeons systematic review and evidence-based guidelines on the use of stereotactic radiosurgery in the treatment of adults with metastatic brain tumors","volume":"84","author":"Graber","year":"2019","journal-title":"Neurosurgery"},{"key":"ref_3","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_4","doi-asserted-by":"crossref","first-page":"753","DOI":"10.1007\/s11760-020-01793-2","article-title":"Joint training of two-channel deep neural network for brain tumor classification","volume":"15","author":"Bodapati","year":"2021","journal-title":"Signal Image Video Process."},{"key":"ref_5","unstructured":"Society, A.C. (2024, May 23). Available online: http:\/\/www.cancer.net\/cancer-types\/brain-tumor\/statistics\/."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.comnet.2019.04.016","article-title":"Effective features to classify ovarian cancer data in internet of medical things","volume":"159","author":"Elhoseny","year":"2019","journal-title":"Comput. Networks"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"29763","DOI":"10.1109\/ACCESS.2018.2880838","article-title":"Trust management techniques for the Internet of Things: A survey","volume":"7","author":"Din","year":"2018","journal-title":"IEEE Access"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1016\/j.neucom.2022.07.005","article-title":"Applicable artificial intelligence for brain disease: A survey","volume":"504","author":"Huang","year":"2022","journal-title":"Neurocomputing"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Hammad, M., ElAffendi, M., Ateya, A.A., and Abd El-Latif, A.A. (2023). Efficient brain tumor detection with lightweight end-to-end deep learning model. Cancers, 15.","DOI":"10.3390\/cancers15102837"},{"key":"ref_10","first-page":"e17","article-title":"A review on automatic detection of brain tumor using computer aided diagnosis system through MRI","volume":"5","author":"Meera","year":"2018","journal-title":"EAI Endorsed Trans. Energy Web"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Zulfiqar, F., Bajwa, U.I., and Mehmood, Y. (2023). Multi-class classification of brain tumor types from MR images using EfficientNets. Biomed. Signal Process. Control, 84.","DOI":"10.1016\/j.bspc.2023.104777"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Asiri, A.A., Iqbal, A., Ferzund, J., Ali, T., Aamir, M., Alshamrani, K.A., Alshamrani, H.A., Alqahtani, F.F., Irfan, M., and Alshehri, A.H. (2022). A Novel Hybrid Machine Learning Approach for Classification of Brain Tumor Images. Comput. Mater. Contin., 73.","DOI":"10.32604\/cmc.2022.029000"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"57","DOI":"10.1016\/j.comcom.2020.11.013","article-title":"Class consistent and joint group sparse representation model for image classification in internet of medical things","volume":"166","author":"Gao","year":"2021","journal-title":"Comput. Commun."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"D\u00edaz-Pernas, F.J., Mart\u00ednez-Zarzuela, M., Ant\u00f3n-Rodr\u00edguez, M., and Gonz\u00e1lez-Ortega, D. (2021). A deep learning approach for brain tumor classification and segmentation using a multiscale convolutional neural network. Healthcare, 9.","DOI":"10.3390\/healthcare9020153"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"012115","DOI":"10.1088\/1757-899X\/1055\/1\/012115","article-title":"Brain tumor detection from MRI images using deep learning techniques","volume":"Volume 1055","author":"Brindha","year":"2021","journal-title":"Proceedings of the IOP Conference Series: Materials Science and Engineering"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1731","DOI":"10.1002\/ima.22554","article-title":"Three-class classification of brain magnetic resonance images using average-pooling convolutional neural network","volume":"31","author":"Kakarla","year":"2021","journal-title":"Int. J. Imaging Syst. Technol."},{"key":"ref_17","first-page":"69","article-title":"Internet of medical things with cloud-based e-health services for brain tumour detection model using deep convolution neural network","volume":"16","author":"Ganesan","year":"2020","journal-title":"Electron. Gov. Int. J."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Kibriya, H., Masood, M., Nawaz, M., Rafique, R., and Rehman, S. (2021, January 15\u201317). Multiclass brain tumor classification using convolutional neural network and support vector machine. Proceedings of the 2021 Mohammad Ali Jinnah University International Conference on Computing (MAJICC), Karachi, Pakistan.","DOI":"10.1109\/MAJICC53071.2021.9526262"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"29847","DOI":"10.1007\/s11042-022-12977-y","article-title":"Multiclass classification of brain tumors using a novel CNN architecture","volume":"81","author":"Kibriya","year":"2022","journal-title":"Multimed. Tools Appl."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Tripathi, P.C., and Bag, S. (2022). A computer-aided grading of glioma tumor using deep residual networks fusion. Comput. Methods Programs Biomed., 215.","DOI":"10.1016\/j.cmpb.2021.106597"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"e6541","DOI":"10.1002\/cpe.6541","article-title":"Three-class brain tumor classification from magnetic resonance images using separable convolution based neural network","volume":"34","author":"Isunuri","year":"2022","journal-title":"Concurr. Comput. Pract. Exp."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"8357","DOI":"10.1007\/s12652-020-02568-w","article-title":"Automated categorization of brain tumor from mri using cnn features and svm","volume":"12","author":"Deepak","year":"2021","journal-title":"J. Ambient Intell. Humaniz. Comput."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"108196","DOI":"10.1016\/j.compeleceng.2022.108196","article-title":"Brain image identification and classification on Internet of Medical Things in healthcare system using support value based deep neural network","volume":"102","author":"Vankdothu","year":"2022","journal-title":"Comput. Electr. Eng."},{"key":"ref_24","first-page":"32","article-title":"IoT framework for brain tumor classification using optimized CNN-MRFO model","volume":"11","author":"Kamil","year":"2021","journal-title":"Am. J. Bioinform. Res."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"1793","DOI":"10.32604\/csse.2023.024674","article-title":"Brain Tumor Diagnosis Using Sparrow Search Algorithm Based Deep Learning Model","volume":"44","author":"Rajathi","year":"2023","journal-title":"Comput. Syst. Sci. Eng."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Kurdi, S.Z., Ali, M.H., Jaber, M.M., Saba, T., Rehman, A., and Dama\u0161evi\u010dius, R. (2023). Brain tumor classification using meta-heuristic optimized convolutional neural networks. J. Pers. Med., 13.","DOI":"10.3390\/jpm13020181"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Biswas, A., and Islam, M.S. (2023). A Hybrid Deep CNN-SVM Approach for Brain Tumor Classification. J. Inf. Syst. Eng. Bus. Intell., 9.","DOI":"10.20473\/jisebi.9.1.1-15"},{"key":"ref_28","unstructured":"Hamada, A. (2024, July 12). Br35H :: Brain Tumor Detection 2020. Available online: https:\/\/www.kaggle.com\/datasets\/ahmedhamada0\/brain-tumor-detection."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Filatov, D., and Ahmad Hassan Yar, G.N. (2022). Brain tumor diagnosis and classification via pre-trained convolutional neural networks. medRxiv.","DOI":"10.1101\/2022.07.18.22277779"},{"key":"ref_30","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_31","doi-asserted-by":"crossref","unstructured":"Rajkumar, S., Karthick, K., Selvanathan, N., Saravanan, U.B., Murali, M., and Dhiyanesh, B. (2021, January 8\u201310). Brain Tumor Detection Using Deep Learning Neural Network for Medical Internet of Things Applications. Proceedings of the 2021 6th International Conference on Communication and Electronics Systems (ICCES), Coimbatore, India.","DOI":"10.1109\/ICCES51350.2021.9489187"}],"container-title":["Information"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2078-2489\/15\/8\/489\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T15:37:41Z","timestamp":1760110661000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2078-2489\/15\/8\/489"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,8,16]]},"references-count":31,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2024,8]]}},"alternative-id":["info15080489"],"URL":"https:\/\/doi.org\/10.3390\/info15080489","relation":{},"ISSN":["2078-2489"],"issn-type":[{"value":"2078-2489","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,8,16]]}}}