Author: Mostafa Abdelhamed. Domain: Medical Image Analysis, Deep Learning, Healthcare AI
Chest diseases such as pneumonia and tuberculosis remain among the leading causes of morbidity and mortality worldwide. Chest X-ray imaging is a primary diagnostic tool; however, its interpretation depends heavily on expert radiologists, making the process time-consuming and susceptible to inter-observer variability. This project presents an end-to-end deep learning framework for automated chest disease classification using medical imaging data.
The proposed system integrates data version control, model training, experiment tracking, and web-based deployment into a reproducible pipeline. The framework aims to improve diagnostic efficiency and consistency while providing a scalable foundation for future research in computer-aided diagnosis (CAD) systems.
Medical Image Analysis, Chest X-ray Classification, Deep Learning, Computer-Aided Diagnosis, TensorFlow, MLflow, DVC
Recent advancements in deep learning have demonstrated significant potential in automating disease detection from medical images. Convolutional Neural Networks (CNNs), in particular, have shown strong performance in extracting hierarchical visual features from radiographic data. Despite these advances, many implementations lack reproducibility, structured experimentation, and deployment readiness.
This project addresses these limitations by presenting a complete, modular, and reproducible pipeline for chest disease classification, covering the entire lifecycle from data management to deployment.
The primary objectives of this project are:
- To design an end-to-end deep learning pipeline for chest disease classification
- To ensure experiment reproducibility using data and model versioning
- To integrate systematic experiment tracking and performance evaluation
- To deploy the trained model via a lightweight web interface for inference
- To provide a research-ready codebase suitable for extension and benchmarking
The proposed framework consists of the following components:
-
Data Ingestion and Versioning
- Dataset management using DVC
- Reproducible data pipelines
-
Model Training and Evaluation
- Deep learning model implemented using TensorFlow
- Performance tracking and logging with MLflow
-
Experiment Management
- Centralized logging of metrics, parameters, and artifacts
-
Deployment Layer
- Flask-based web application for inference
- Dockerized environment for portability
- Python 3.9+
- TensorFlow 2.12.0
- Pandas
- Flask
- MLflow 2.2.2
- DVC
- GDown
This project utilizes the Google Gemini Large Language Model for auxiliary functionality.
Create a .env file in the project root directory:
API_KEY=your_api_key_hereSecurity Notice: API keys must not be committed to version control systems.
- Dataset versions are tracked using DVC
- Model parameters and evaluation metrics are logged using MLflow
- Docker ensures environment consistency across systems
This design supports reproducible research and facilitates experimental comparison.
- Evaluation limited to a specific dataset
- Single-model architecture
- Multi-class and multi-label classification
- Integration of explainability methods (Grad-CAM, SHAP)
- Clinical validation using real-world datasets
- Deployment as a cloud-based inference service
This system is intended as a decision-support tool and not as a replacement for professional medical diagnosis. Clinical deployment requires rigorous validation, regulatory approval, and adherence to healthcare data privacy standards.
Mostafa Abdelhamed Email: abdelhamedmostafa190@gmail.com
Special thanks to the open-source community and contributors whose tools and research made this project possible. Your work continues to inspire innovation in AI-driven healthcare solutions.