Unlocking AI Transparency: Advanced Explainable AI (XAI) Techniques

🎯 Project Overview

This research focuses on transforming AI from a black box into an interpretable white box system. The aim is to improve the transparency of AI models, enhancing their decision-making processes through advanced visualization and feature extraction techniques. This repository showcases my contributions to this goal, particularly in the areas of Grad-CAM visualization and feature extraction pipelines.

🔬 Core Research Focus

1. Grad-CAM Visualization Implementation

• Objective: Improve model interpretability by visualizing decision-making processes.
• Techniques Involved:
  • Gradient-weighted Class Activation Mapping (Grad-CAM) to generate visual explanations for CNNs.
  • Highlighting key regions in images that influence model predictions.
  • Advanced visualization to make the decision layer of the CNN transparent.

2. Feature Extraction Pipeline

• Objective: Enhance data preprocessing and feature extraction for medical imaging.
• Techniques Involved:
  • Development of a custom preprocessing pipeline to improve the quality of input data.
  • Implementation of feature engineering techniques to enhance model accuracy and reliability.

📊 Research Outcomes

Model Interpretability

• Successfully implemented Grad-CAM visualization to better understand model decisions.
• Increased transparency of AI systems, validated through applications in medical imaging.

Pipeline Efficiency

• Optimized feature extraction process and data preprocessing techniques.
• Achieved a significant improvement in model performance metrics.

🔒 Data Privacy Notice

This repository excludes the medical dataset due to privacy regulations. All research was conducted with legally obtained and processed medical imaging data.

📂 Literature Survey

Explore the comprehensive literature survey conducted as part of this research. It includes key findings, previous studies, and references that laid the foundation for this project:

View the Literature Survey

🚀 Innovation Highlights

Advanced Visualization

• Real-time activation mapping with Grad-CAM.
• Transparent decision-making process visualization.
• Making complex AI decisions interpretable.

Feature Engineering

• Custom slicing algorithms for improved data preprocessing.
• Quality-focused optimization of input data.

📈 Future Development

• Further development of visualization techniques.
• Enhanced interpretation methods for even more transparent AI systems.
• Advanced feature extraction algorithms for improved performance.

🎓 Research Publication

• Paper under conference review focusing on the novel implementation of XAI techniques.
• Emphasis on improving AI interpretability in medical imaging applications.