Our system automates the analysis of chest X-ray images, reducing manual workload.
Enhance efficiency and accuracy of pneumonia detection with deep learning models.
Designed to be scalable and adaptable to various clinical settings.
A powerful programming language used for building machine learning models and backend development.
An open-source machine learning framework for building and training deep learning models.
An open-source computer vision library used for image and video processing tasks.
A Python library used for data manipulation and analysis, particularly for structured data.
A Python plotting library used for creating static, animated, and interactive visualizations.
A Python data visualization library based on Matplotlib, used for statistical graphics.
A comprehensive dataset of chest X-ray images meticulously labeled by medical experts.
Preprocessing steps such as noise removal, image resizing, and normalization.
Applying transformations like rotation, flipping, and scaling to increase dataset diversity.
Dividing the dataset into training and testing sets for model evaluation.
Scaling pixel values to a range suitable for neural network training.
Applying transformations like rotation, flipping, and scaling to increase dataset diversity.
Expanding the dataset with diverse chest X-ray images to improve the model's generalization capabilities.
Investigating the use of state-of-the-art deep learning techniques, such as transfer learning and ensemble methods, to further enhance the model's performance.
Integrating the system with EHRs to enable seamless access to patient data and facilitate more informed clinical decision-making.
The primary goal of our project is to develop an automated system for chest X-ray analysis to aid in the detection of pneumonia.
Our project leverages Next.js and Tailwind CSS for front-end development, and Python with TensorFlow for deep learning model development.
The accuracy of the system depends on various factors, including the quality of the dataset and the performance of the deep learning models. Our system undergoes rigorous evaluation and validation to ensure reliable results.
Yes, our project is designed to be scalable and adaptable to various clinical settings. The system architecture and model design allow for seamless integration and deployment in real-world scenarios.
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