VGX: VGG19-Based Gradient Explainer Interpretable Architecture for Brain Tumor Detection in Microscopy Magnetic...

Area of Science:

• Medical Image Analysis
• Artificial Intelligence in Oncology
• Neuroscience

Background:

• Deep learning has revolutionized medical image analysis, particularly for brain tumor detection.
• Accurate identification of brain tumors is crucial for effective patient treatment and management.
• Existing methods may lack interpretability, posing challenges for clinical trust.

Purpose of the Study:

• To develop a robust automatic method for microbrain tumor identification using deep learning.
• To leverage multi-modal data from Microscopy Magnetic Resonance Imaging (MMRI) for enhanced feature extraction.
• To integrate explainable AI (XAI) for interpreting the model's diagnostic decisions.

Main Methods:

• Utilized the VGG16 deep learning model for brain tumor classification.
• Employed MMRI scans to capture detailed, multi-modal imaging features.
• Implemented pre-processing techniques to optimize data quality and model efficiency.
• Integrated a gradient explainer for interpreting classification results.

Main Results:

• The modified VGG19 model achieved a high validation accuracy of 98.81%.
• The integrated XAI approach provided interpretable insights into the model's decision-making process.
• Comparative analysis demonstrated the superiority of the proposed explainer over other XAI techniques.

Conclusions:

• The proposed deep learning model demonstrates significant potential for precise and effective brain tumor diagnosis.
• The integration of XAI enhances the clinical applicability and trustworthiness of AI-driven diagnostic systems.
• This approach offers a promising direction for advancing automated brain tumor detection and interpretation.