ExplainableAI-RareDisease
Project Overview: The "ExplainableAI-RareDisease" project focuses on utilizing deep learning techniques, particularly the ResNet-50 architecture, to improve the diagnostic accuracy of rare diseases through medical imaging. This project classifies chest X-ray images into four categories: COVID-19, Lung Opacity, Normal, and Viral Pneumonia.
Key Features:
Deep Learning: Utilizes the ResNet-50 convolutional neural network (CNN) for image classification. Dataset: The dataset includes 16,930 images for training, 2,116 images for validation, and 2,119 images for testing. Performance: The model shows progressive improvement in accuracy across training epochs, with validation accuracy peaking at 55.25%. However, the test accuracy of 32.74% highlights challenges like overfitting and generalization. Explainable AI: Focuses on making the AI model's decisions more interpretable for healthcare professionals. Project Objectives:
Enhance the accuracy of rare disease diagnoses using AI. Improve the efficiency of radiologists in diagnosing rare diseases with automated image classification. Address challenges such as data diversity, overfitting, and model generalization. Practical Implications: This project has the potential to assist radiologists by automating the classification of chest X-ray images, thus speeding up the diagnostic process. However, it is essential to understand the limitations of AI models and regularly update them to ensure accuracy and reliability in clinical settings.
Challenges:
Overfitting due to insufficient data diversity. Generalization issues when applying the model to unseen data. Need for continuous model refinement to achieve robust performance across diverse datasets. Future Work:
Implement data augmentation techniques to improve model generalization. Explore hyperparameter tuning and advanced model architectures. Collaborate with clinical experts to validate model predictions and adhere to ethical guidelines in healthcare. Technologies Used:
Python TensorFlow/Keras ResNet-50 Architecture Medical Imaging (Chest X-ray) Setup Instructions:
Clone the repository: bash Copy code git clone https://github.com/yourusername/ExplainableAI-RareDisease.git Install the required dependencies: bash Copy code pip install -r requirements.txt Train the model using the provided dataset and scripts: bash Copy code python train_model.py License: This project is licensed under the MIT License - see the LICENSE file for details.
Acknowledgments:
ResNet-50 model: Original research by Kaiming He et al. Dataset: https://www.kaggle.com/datasets/preetviradiya/covid19-radiography-dataset. Contributions from clinical experts and researchers in AI-based medical imaging.