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Obagbuwa, Ibidun Christiana Ndogmo, Jean-Claude Netshikweta, Rendani Netshamutshedzi, Ndivhuwo 2026 2026-06-17T17:14:53Z 2026-06-17T17:14:53Z 2026-05-19 Netshamutshedzi, N. 2026. Improving Computational Efficiency of MRI Brain Tumour Analysis Using Hybrid Machine Learning Models. . . https://univendspace.univen.ac.za/handle/11602/3196 Netshamutshedzi N. Improving Computational Efficiency of MRI Brain Tumour Analysis Using Hybrid Machine Learning Models. []. , 2026 [cited yyyy month dd]. Available from: Netshamutshedzi, N. (2026). <i>Improving Computational Efficiency of MRI Brain Tumour Analysis Using Hybrid Machine Learning Models</i>. (). . Retrieved from Netshamutshedzi, Ndivhuwo. <i>"Improving Computational Efficiency of MRI Brain Tumour Analysis Using Hybrid Machine Learning Models."</i> ., , 2026. TY - Dissertation AU - Netshamutshedzi, Ndivhuwo AB - Brain tumor is a critical challenge in medical diagnostics, worsen by the high mortality rate and prevalence worldwide of the disease. Accurate and early detection is paramount to improving patient outcomes. This study focuses on evaluating the usefulness of machine learning (ML) and deep learning (DL) models in classifying brain tumor and non-tumor cases using a dataset sourced from Kaggle. After preprocessing, the dataset was analyzed using Support Vector Machines (SVM), VGG-19, and YOLOv10 models. Metrics including accuracy, precision, recall, F1-score, and ROC-AUC were utilized to evaluate the model's effectiveness. The findings reveal that hybrid models, particularly SVM+VGG-19, excel in tumor classifi cation, achieving an outstanding accuracy of 99.80% and a ROC-AUC of 98.01%. These models not only deliver superior accuracy but also require less training time compared to standalone models like SVM, VGG-19, or YOLOv10, employ explainable AI techniques such as LIME and SHAP to explain the models. By combining high precision with relatively low computational time, the SVM+VGG-19 hybrid model emerges as a robust way to deal with the MRI brain tumor segmentation problem, making it highly suitable for real-time image analysis. DA - 2026-05-19 DB - ResearchSpace DP - Univen KW - Deep learning KW - Machine learning KW - UCTD KW - Support Vector Machine KW - VGG-19 KW - Convolutional neural network KW - Yolovlo KW - Medical images KW - LIME KW - SHAP LK - https://univendspace.univen.ac.za PY - 2026 T1 - Improving Computational Efficiency of MRI Brain Tumour Analysis Using Hybrid Machine Learning Models TI - Improving Computational Efficiency of MRI Brain Tumour Analysis Using Hybrid Machine Learning Models UR - ER - M.Sc. in e-Science Department of Mathematical and Computational Sciences Brain tumor is a critical challenge in medical diagnostics, worsen by the high mortality rate and prevalence worldwide of the disease. Accurate and early detection is paramount to improving patient outcomes. This study focuses on evaluating the usefulness of machine learning (ML) and deep learning (DL) models in classifying brain tumor and non-tumor cases using a dataset sourced from Kaggle. After preprocessing, the dataset was analyzed using Support Vector Machines (SVM), VGG-19, and YOLOv10 models. Metrics including accuracy, precision, recall, F1-score, and ROC-AUC were utilized to evaluate the model's effectiveness. The findings reveal that hybrid models, particularly SVM+VGG-19, excel in tumor classifi cation, achieving an outstanding accuracy of 99.80% and a ROC-AUC of 98.01%. These models not only deliver superior accuracy but also require less training time compared to standalone models like SVM, VGG-19, or YOLOv10, employ explainable AI techniques such as LIME and SHAP to explain the models. By combining high precision with relatively low computational time, the SVM+VGG-19 hybrid model emerges as a robust way to deal with the MRI brain tumor segmentation problem, making it highly suitable for real-time image analysis. 1 online resource (iv, 108 leaves): color illustrations en PDF University of Venda Deep learning Machine learning Support Vector Machine VGG-19 Convolutional neural network Yolovlo Medical images LIME SHAP UCTD Improving Computational Efficiency of MRI Brain Tumour Analysis Using Hybrid Machine Learning Models Dissertation embargo