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Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

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Enhanced brain tumor classification framework using deep learning.

Ramesh Babu Vure¹, Lalitha Kumari Pappala²

¹VIT-AP University, Amaravati, Andhra Pradesh, India.

Scientific Reports

|October 14, 2025

Summary

This summary is machine-generated.

This study introduces an advanced deep learning framework using Generative Adversarial Networks (GANs) and DMFN for improved brain tumor classification, achieving 98.36% accuracy on the BRATS2021 dataset.

Keywords:

Brain tumor classification Deep learning GAN PCA-PSO ResNet18

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Published on: August 16, 2020

Area of Science:

Medical Imaging
Artificial Intelligence
Oncology

Background:

Brain tumor diagnosis requires accurate tools for early detection and classification.
Traditional methods struggle with early-stage tumor detection and classification.
Deep learning models face challenges with complex datasets and limited labeled data.

Purpose of the Study:

To develop an advanced deep learning framework for accurate brain tumor classification.
To enhance classification accuracy for glioma, meningioma, no tumor, and pituitary tumors.
To address data limitations using data augmentation techniques.

Main Methods:

Utilized Generative Adversarial Networks (GANs) for data augmentation.
Employed ResNet18 for effective feature extraction from medical images.
Developed a Deep Multi-view Fusion Network (DMFN) model using multiple ResNet18 instances for classification.
Incorporated PCA-PSO for feature selection.

Main Results:

Achieved a validation accuracy of 98.36% on the BRATS2021 dataset.
Reduced training loss to 0.1963 and validation loss to 0.1382.
Demonstrated significant improvement over existing techniques in brain tumor classification.

Conclusions:

The proposed framework shows significant potential for advancing brain tumor diagnostics.
The combination of GAN, PCA-PSO, and DMFN offers a robust approach for medical image analysis.
This framework can be applied to other medical imaging tasks for improved clinical outcomes.