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Brain Infarct Segmentation and Registration on MRI or CT for Lesion-symptom Mapping

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Efficient and Accurate Brain Tumor Classification Using Hybrid MobileNetV2-Support Vector Machine for Magnetic

Mohammed Jajere Adamu^1,2,3, Halima Bello Kawuwa⁴, Li Qiang¹

¹Department of Electronic Science and Technology, School of Microelectronics, Tianjin University, Tianjin 300072, China.

|January 8, 2025

Summary

This summary is machine-generated.

A new hybrid model using MobileNetV2 and Support Vector Machine (SVM) accurately classifies brain tumors from MRI scans. This efficient approach improves diagnostic precision and computational performance for clinical applications.

Keywords:

MR images MobileNetV2 SVM brain tumor classification machine and deep learning

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Area of Science:

Medical Imaging
Artificial Intelligence in Medicine
Computational Biology

Background:

Magnetic Resonance Imaging (MRI) is crucial for non-ionizing brain tumor diagnosis.
Increasing brain tumor incidence necessitates reliable diagnostic tools to prevent misdiagnosis.
Machine learning offers advanced diagnostics but faces accuracy-efficiency challenges.

Purpose of the Study:

To develop a hybrid machine learning model for accurate and efficient brain tumor classification.
To integrate MobileNetV2 for feature extraction and Support Vector Machine (SVM) for classification.
To address the critical challenge of balancing accuracy and computational efficiency in medical diagnostics.

Main Methods:

A hybrid model combining MobileNetV2 (feature extraction) and Support Vector Machine (SVM) (classification) was developed.
The model was trained and validated on the Kaggle MRI brain tumor dataset (7023 images).
Dataset included glioma, meningioma, pituitary tumor, and no tumor categories.

Main Results:

The hybrid model achieved high Area Under the Curve (AUC) scores: 0.99 (glioma), 0.97 (meningioma), 1.0 (pituitary tumor, no tumor).
The MobileNetV2-SVM model demonstrated improved classification accuracy.
The model showed reduced computational overhead, enhancing clinical suitability.

Conclusions:

The MobileNetV2-SVM hybrid model shows significant potential for improving brain tumor diagnostics.
The model offers a balance between diagnostic precision and computational efficiency.
This efficient, accurate model could enhance clinical outcomes, especially in resource-limited settings.