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README.md

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----
-license: apache-2.0
----
+---
+license: mit
+language: en
+metrics:
+  - Accuracy
+  - Precision
+  - Recall
+  - F1-Score
+library_name: TensorFlow
+pipeline_tag: image-classification
+tags:
+  - Deep Learning
+  - Pneumonia Detection
+  - CNN
+  - Chest X-ray
+---
+
+
+# Chest X-ray Pneumonia Detection using Deep Learning
+
+## Overview
+This project leverages deep learning techniques, specifically convolutional neural networks (CNNs), to classify chest X-ray images for pneumonia detection. The primary goal is to develop a system that can accurately identify pneumonia cases from X-ray scans, aiding medical professionals in their diagnostic process.
+
+## Technologies Used
+- **Python 3.9**
+- **TensorFlow 2.7**
+- **OpenCV 4.5.5**
+- **Pandas 1.3.5**
+- **Matplotlib 3.6.2**
+- **Seaborn 0.11.2**
+
+## Key Features
+- **Automated Chest X-ray Analysis:** Automates the analysis of chest X-ray images, reducing manual workload for healthcare providers.
+- **Improved Efficiency and Accuracy:** Enhances the efficiency and accuracy of pneumonia detection, leading to faster and more reliable diagnoses.
+- **Scalability and Adaptability:** Designed to be scalable and adaptable to various clinical settings, ensuring practical implementation in real-world scenarios.
+
+## Data Preparation
+The project utilizes a comprehensive dataset of chest X-ray images, meticulously labeled by medical experts. This dataset serves as the foundation for training and evaluating our deep learning models. The dataset can be accessed [here on Kaggle](https://www.kaggle.com/datasets/paultimothymooney/chest-xray-pneumonia).
+
+## Model Development and Training
+We employ Convolutional Neural Networks (CNNs) as our primary deep learning model architecture. These models are trained on the prepared dataset, learning to identify distinctive patterns and features associated with pneumonia in chest X-ray images.
+
+## Evaluation and Validation
+The trained models are thoroughly evaluated using various metrics such as accuracy, precision, recall, and F1-score. This evaluation process ensures the reliability and effectiveness of our system in real-world scenarios.
+
+## User Interface and Integration
+The project incorporates a user-friendly interface for seamless interaction with the trained models. This interface allows healthcare professionals to upload chest X-ray images and receive automated predictions regarding the presence or absence of pneumonia.
+
+## Future Directions
+- **Incorporating Additional Data Sources:** Expanding the dataset with diverse chest X-ray images to improve the model's generalization capabilities.
+- **Exploring Advanced Deep Learning Techniques:** 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.
+- **Integration with Electronic Health Records (EHRs):** Integrating the system with EHRs to enable seamless access to patient data and facilitate more informed clinical decision-making.
+
+## Conclusion
+This project demonstrates the potential of deep learning in automating chest X-ray analysis for pneumonia detection. By providing accurate and timely results, our system aims to assist healthcare professionals in delivering better patient care and improving overall healthcare outcomes.
+
+## Deployment
+The model has been deployed using TensorFlow.js and Next.js in a web application accessible at [https://pneumonia-classifier-nextjs.vercel.app/](https://pneumonia-classifier-nextjs.vercel.app/).
+
+## Main Repository
+For detailed information about the project, including code and documentation, visit the [main repository on GitHub](https://github.com/Aliabdo6/Pneumonia-Classifier-ml).