ELISA - CHEST X-RAY REPORT

System Overview

The Chest X-ray Anomalies Detection System utilizes a deep learning algorithm to analyze chest X-rays and predict the presence of 14 anomalies. The system is accessible via the web, allowing users with an internet connection to upload and analyze chest X-ray images.

The architecture of the system is designed to be modular, facilitating easy integration with other projects. We have implemented a conversion pipeline using ONNX to transform models trained in PyTorch or other frameworks. This pipeline enables the translation of models into frameworks with browser support, such as TensorFlow.js, ensuring compatibility across different platforms.

Key features of the system include:

• Deep learning-based analysis for accurate anomaly detection.
• Modular design for flexibility and future scalability.
• Compatibility with various frameworks through the ONNX conversion pipeline.
• Easy accessibility via web interface.