Improving Computational Efficiency of MRI Brain Tumour Analysis Using Hybrid Machine Learning Models

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.

Author dc:creator

• Netshamutshedzi, Ndivhuwo

Advisors dc:contributor.advisor

• Obagbuwa, Ibidun Christiana
• Ndogmo, Jean-Claude
• Netshikweta, Rendani

• Deep learning
• Machine learning
• Support Vector Machine
• VGG-19
• Convolutional neural network
• Yolovlo
• Medical images
• LIME
• SHAP
• UCTD

Statement dc:rights

• University of Venda

Language dc:language.iso

en

Repository record dc:identifier.uri

https://univendspace.univen.ac.za/handle/11602/3196

OAI identifier oai:identifier

oai:univendspace.univen.ac.za:11602/3196