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Mask region-based convolutional neural network and VGG-16 inspired brain tumor segmentation.

Niha Kamal Basha¹, Christo Ananth^2,3, K Muthukumaran⁴

¹School of Computer Science and Engineering, Vellore Institute of Technology (VIT), Vellore, Tamil Nadu, India.

Scientific Reports

|July 30, 2024

Summary

This study introduces an effective brain tumour detection method using deep learning on MRI scans. The proposed model achieved nearly 99% accuracy and sensitivity, significantly improving diagnostic capabilities.

Keywords:

Brain tumor segmentation Grad-CAM Inception V3 R-CNN mask ResNet50 Transfer learning VGG16

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Area of Science:

Medical Imaging Analysis
Artificial Intelligence in Healthcare
Neuro-oncology Diagnostics

Background:

Brain tumour segmentation is crucial for accurate diagnosis and treatment planning.
Magnetic Resonance Imaging (MRI) is the standard for detecting brain abnormalities.
Existing methods require enhancement for improved precision in tumour localization.

Purpose of the Study:

To develop an effective and highly accurate method for brain tumour detection using deep learning.
To improve diagnostic accuracy for medical professionals.
To leverage advanced AI techniques for precise tumour segmentation and identification.

Main Methods:

Utilized region-based Convolutional Neural Network (R-CNN) masks for segmentation.
Employed Grad-CAM and transfer learning for effective tumour detection.
Trained models using Inception V3, VGG-16, and ResNet-50 architectures on the Brain MRI Images for Brain Tumour Detection dataset.

Main Results:

The transfer learning-based model demonstrated high sensitivity and accuracy.
Achieved approximately 99% accuracy and sensitivity, outperforming current methods.
Performance was evaluated using recall, specificity, sensitivity, accuracy, precision, and F1 score.

Conclusions:

The proposed method offers a significant advancement in brain tumour detection accuracy.
Deep learning models, particularly with VGG-16 influence, show strong potential for clinical application.
The approach aids clinicians in making highly accurate diagnoses, improving patient outcomes.