Brain Tumor Detection from MRI Images Using an Ensemble-Based Machine Learning Framework
Abstract
The early detection of brain tumors from MRI images is critical for effective treatment planning. Still, manual analysis of these images is time-consuming and prone to inter-observer variability. This paper suggests a machine learning framework for automated brain tumor detection that uses an ensemble of classifiers to make it more accurate and reliable. The suggested framework combines Support Vector Machine (SVM), Random Forest (RF), and k-Nearest Neighbor (k-NN) classifiers. It uses a majority voting method at the decision level to make final predictions. The model uses both handcrafted texture features from the Gray-Level Co-occurrence Matrix (GLCM) and deep features from a pre-trained ResNet50 model to make it more effective at distinguishing between things. The framework was tested using three publicly available MRI datasets: Figshare, SARTAJ, and BR35H. These datasets had a total of 9,826 images. The ensemble model got 95.2% correct, with 94.6%, 94.1%, and 94.3% for precision, recall, and F1-score, respectively. This was better than any of the individual classifiers. The area under the curve (AUC) was also 0.97, which means it was very good at telling the difference between things. The experimental results demonstrate that the ensemble approach not only delivers a robust solution but also ensures computational efficiency, rendering it appropriate for clinical applications. This framework shows that it could be used in computer-aided diagnosis systems to detect brain tumors in real time and perform better across different datasets. The suggested ensemble-based framework is a scalable, efficient, and reliable way to use MRI to find brain tumors. It gets around the problems that single classifiers have in medical imaging.
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References
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