Project Overview
This repository hosts the code and documentation for an advanced deep learning project aimed at automating the diagnosis of pneumonia from chest X-ray images. Utilizing the powerful capabilities of Deep Convolutional Neural Networks (CNNs), this project represents a significant step towards integrating AI in medical diagnostics.
Motivation
Pneumonia is a leading cause of illness worldwide. Rapid and accurate diagnosis is critical for effective treatment, particularly in regions with limited medical resources. This project seeks to leverage the power of AI to assist radiologists and healthcare professionals in diagnosing pneumonia more efficiently and accurately.
Technology Stack
Deep Learning Framework: PyTorch Data Handling: NumPy, Pandas Image Processing: OpenCV, PIL Model Evaluation and Visualization: Matplotlib, Seaborn, Plotly Environment: Jupyter Notebook, Docker (for containerization) Features
Data Preprocessing: Robust techniques for image normalization, augmentation, and transformation to enhance model training. Model Architecture: Implementation of a state-of-the-art CNN architecture specifically tuned for medical image analysis. Training Pipeline: Efficient training loops with checkpointing, logging, and dynamic learning rate adjustments. Evaluation Metrics: Comprehensive metrics to assess model performance, including accuracy, sensitivity, specificity, and F1 score. Interpretability: Tools to visualize what the model is learning and how it is making predictions, fostering trust in AI diagnostics.