Automated Brain Tumor Detection Using Hybrid Machine Learning Models
DOI:
https://doi.org/10.68337/cpsm.v1.i1.2026-005Keywords:
Brain tumor detection, classification, support vector machine, convolutional neural network, MobileNetV2, hybrid machine learning modelsAbstract
Brain tumors are among the most dangerous and life-threatening neurological diseases, and early detection is essential for successful treatment and improved patient survival. Magnetic resonance imaging (MRI) is the most widely used modality for the rapid diagnosis of brain tumors, but the accurate segmentation and interpretation of MRI images remain challenging. Deep learning (DL) has recently brought significant progress in the identification and categorization of brain tumors. This paper proposes a hybrid machine learning model for brain tumor detection in which a pretrained MobileNetV2 convolutional neural network (CNN) extracts deep features from MRI images and a linear support vector machine (SVM) classifies them into four classes: glioma, meningioma, pituitary tumor, and no tumor. The performance of the model is reported with class-wise precision and recall and a confusion matrix.
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The data supporting the findings of this study are available from the corresponding author upon reasonable request.Conference Proceedings Volume
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Articles published in Conference Proceedings in Science and Management are made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0). Authors retain copyright in their work and grant the journal first publication rights. Readers are free to share and adapt the published material for any purpose, including commercial use, provided appropriate credit is given to the author, a link to the license is provided, and any changes are indicated.
