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Deep Belief VGG-16 Hybrid Model for Brain Tumor Classification Using MRI Images.

G V Sriramakrishnan¹, Telagarapu Prabhakar², Balajee Maram³

¹Department of Computer Science and Engineering, Saveetha Institute of Medical and Technical Sciences (SIMATS), Saveetha University, Chennai, Tamil Nadu, India.

NMR in Biomedicine

|May 19, 2025

Summary

This summary is machine-generated.

This study introduces a novel Deep Belief-Visual Geometry Group-16 (DB-VGG-16) model for accurate brain tumor classification from MRI scans. The proposed method enhances early detection and diagnosis, crucial for effective treatment planning.

Keywords:

VGG‐16 bilateral filter brain tumors classification deep belief VG‐16 deep belief network

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Area of Science:

Medical Imaging
Artificial Intelligence
Oncology

Background:

Accurate brain tumor diagnosis is critical for treatment planning.
Current classification methods face challenges with MRI scan variations, impacting accuracy and early detection.

Purpose of the Study:

To propose and evaluate a novel Deep Belief-Visual Geometry Group-16 (DB-VGG-16) model for brain tumor classification using MRI.
To improve the accuracy and reliability of brain tumor detection and classification.

Main Methods:

Utilized Deep Belief Network (DBN) and Visual Geometry Group-16 (VGG-16) for classification.
Employed image preprocessing with bilateral filtering and segmentation via morphological operations.
Extracted statistical and texture features from segmented tumor regions.

Main Results:

The DB-VGG-16 model achieved high performance metrics on the figshare dataset.
Maximal specificity of 0.918, accuracy of 0.928, sensitivity of 0.903, precision of 0.916, and F1-score of 0.910 were recorded.
The model demonstrated effectiveness in classifying brain tumors from MRI scans.

Conclusions:

The proposed DB-VGG-16 model offers a robust and accurate solution for brain tumor classification.
This approach has the potential to significantly aid in early diagnosis and treatment planning for brain tumors.