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Optimizing Tumor Detection in Brain MRI with One-Class SVM and Convolutional Neural Network-Based Feature Extraction.

Azeddine Mjahad¹, Alfredo Rosado-Muñoz¹

¹GDDP, Department Electronic Engineering, School of Engineering, University of Valencia, 46100 Burjassot, Valencia, Spain.

Journal of Imaging

|July 25, 2025

Summary

This summary is machine-generated.

This study developed AI methods for early brain tumor detection using magnetic resonance imaging (MRI). Deep learning models combined with One-Class Support Vector Machines (OCSVM) effectively identified anomalies in imbalanced datasets.

Keywords:

CNNs OCSVM brain tumor detection dimensionality reduction feature-based methods frequency analysis medical imaging

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Area of Science:

Medical Imaging Analysis
Artificial Intelligence in Healthcare
Neuro-oncology

Background:

Early brain tumor detection is crucial for patient outcomes.
Medical imaging datasets often suffer from class imbalance, hindering traditional AI classification.
Developing robust AI for anomaly detection in scarce pathological data is a significant challenge.

Purpose of the Study:

To investigate the efficacy of One-Class Support Vector Machine (OCSVM) combined with deep learning feature extraction for brain tumor anomaly detection.
To compare the performance of various deep learning architectures (DenseNet121, VGG16, MobileNetV2, InceptionV3, ResNet50) and classical methods for feature extraction.
To evaluate a pure Convolutional Neural Network (CNN) approach for direct classification without OCSVM.

Main Methods:

Extracted features from healthy brain MRI images using deep learning architectures and classical techniques.
Trained a One-Class Support Vector Machine (OCSVM) exclusively on features from healthy brain images.
Compared the performance of hybrid CNN-OCSVM models against a pure CNN classification model.

Main Results:

Hybrid CNN-OCSVM models significantly improved anomaly detection over handcrafted features.
DenseNet121 (94.83% accuracy) and VGG16 (95.33% accuracy) showed strong performance in hybrid models.
A pure CNN model achieved superior accuracy (97.83%), demonstrating effective direct feature learning from MRI data.
MobileNetV2 offered a balance between accuracy and computational efficiency.

Conclusions:

AI models, particularly pure CNNs, can reliably detect brain tumor anomalies in imbalanced MRI datasets without pathological labels.
This approach offers a promising solution for clinical settings with limited abnormal samples.
Future work includes optimizing inference time, dataset expansion, and enhancing model explainability for clinical trust.