Automated Technique for Brain Tumor Detection From Magnetic Resonance Imaging Based on Local Features, Ensemble Classification, and YOLOv3.
In this article, the researcher explores an automated approach for detecting a brain tumor using MRI scans of the brain. In underdeveloped countries, many people are dying due to the slow detection process and other negligence of radiologists. People suffer from these diseases due to the slow proces...
| Publicado en: | BioMed Research International Vol. 2025; pp. 1 - 18 |
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| Autores principales: | , , , , , |
| Formato: | diagnostic images equations & formulas research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
11/29/2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=189685555&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 189685555 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 11/29/2025 vid: 2025 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 189685555 189685555 189685555 10.1155/bmri/5531209 189685555 ppf: 1 ppct: 17 formats: fmt: – @attributes: type: T – @attributes: type: C – @attributes: type: P tig: atl: Automated Technique for Brain Tumor Detection From Magnetic Resonance Imaging Based on Local Features, Ensemble Classification, and YOLOv3. aug: au: Arif, Danish Mehmood, Zahid Ullah, Amin Fawad, Ahmad Winberg, Simon Saini, Esha affil: Department of Electrical Engineering,, University of Cape Town,, Rondebosch, South Africa, uct.ac.za sug: subj: Brain Neoplasms Diagnosis Magnetic Resonance Imaging Classification Ensemble Learning Classification Deep Learning Classification Automation Models, Statistical Human Pakistan Diagnostic Errors Prevention and Control Human Error Prevention and Control Uncertainty Prevention and Control Radiologists Machine Learning Algorithms Support Vector Machine Hospitals Descriptive Statistics Sensitivity and Specificity Diagnosis, Computer Assisted DICOM Conceptual Framework ab: In this article, the researcher explores an automated approach for detecting a brain tumor using MRI scans of the brain. In underdeveloped countries, many people are dying due to the slow detection process and other negligence of radiologists. People suffer from these diseases due to the slow process of recognition. Since the number of patients is greater than that of radiologists, there is the possibility of human error, which can cause serious damage. The detection of tumors from magnetic resonance imaging (MRI) data is an important manual task, specifically in terms of the time that the radiologist performs. In this study, the researchers sought to study state‐of‐the‐art techniques to detect normal brain and brain tumors from MRI using machine learning techniques. The main objective of this study is to develop a novel automated technique for brain tumor detection. Through the worldwide consideration of practical literature, it is clear that traditional approaches are insufficient to resolve all uncertainties and problems. Therefore, a novel approach to examining MRI must be adapted. This study proposes two different novel techniques: one that uses ensemble classification and the other that makes use of the deep learning model of YOLOv3. In ensemble classification, two classification algorithms are used which are support vector machine (SVM) and K‐nearest neighbors (KNNs). The YOLOv3 model is used to detect and outline tumor locations in the images. This study used an open‐source dataset and data collected from hospitals in Lahore, Pakistan. The ensemble classifier achieved an overall accuracy of 80.50%, while the YOLOv3 model achieved higher performance with 97.80% accuracy, 97.40% precision, 98.18% recall, and a mean intersection over union (IoU) score of 0.65. These results confirm that YOLOv3 is a useful technique for identifying brain tumors. pubtype: Academic Journal doctype: diagnostic images equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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