Welcome to MediScan – your AI-powered assistant for detecting ocular diseases with unparalleled accuracy. This project is a revolutionary step in medical image analysis, designed to enhance patient care, enable early disease detection, and streamline clinical workflows. 🚀
MediScan leverages Artificial Intelligence and Deep Learning to provide:
- 🩺 Accurate Eye Disease Detection: Identify diseases like cataract, glaucoma, diabetic retinopathy, or determine if the eyes are healthy.
- 📊 Quantitative Analysis: Detailed insights into eye health for better decision-making.
- ⚙️ Scalable Deployment: Designed for healthcare professionals and researchers across diverse settings.
Through automated image interpretation and clinical decision support, MediScan promises improved patient outcomes and efficient workflows. 🧑⚕️💻
Our mission is to:
- Enable early diagnosis and precise detection of ocular diseases.
- Automate the process of medical image interpretation and analysis.
- Create a scalable system that fosters collaboration, compliance, and knowledge sharing.
- Enhanced eye disease diagnosis through AI-powered analysis.
- Improved patient outcomes with early and accurate detection.
- Efficient workflows for healthcare providers, reducing manual interpretation effort.
- 🔧 Set up development tools, environment, and databases.
- 📂 Collect a diverse dataset of eye images to train the AI model.
- 🖼️ Enhance image quality using techniques like noise reduction and normalization.
- 🎯 Implement image segmentation to isolate key regions of interest.
- 🧠 Train a Convolutional Neural Network (CNN) to identify disease patterns in eye images.
- 📈 Optimize model performance using cross-validation.
- 🔌 Seamlessly integrate AI models into a user-friendly interface.
- ✅ Perform rigorous validation to ensure reliability across various datasets.
- 🐛 Address bugs and refine the system for smooth operation.
- 📝 Create comprehensive user guides and technical documentation.
- Input: Upload an eye image.
- Processing: The image undergoes preprocessing, segmentation, and feature extraction.
- Prediction: The trained CNN model predicts the disease with high accuracy.
- Output: The app displays the detected disease, accuracy score, and suggested remedies.
- Python 3.9+
- Libraries:
dash,dash-bootstrap-components,tensorflow,numpy,Pillow,random,pandas,opencv-python,keras,matplotlib,scikit-learn.
- Clone this repository:
git clone https://github.com/your-repo/MediScan.git cd MediScan - Install dependencies:
pip install -r requirements.txt
- Run the application:
python main.py
- Open your browser and navigate to:
http://127.0.0.1:8050/
- Upload eye images for analysis. For example:
-
) -
Disease detected: Cataract
-
Accuracy: 98.82%
-
Suggested Remedy:
"Surgery is the only way to get rid of a cataract."
[Upload Image] --> [Preprocessing] --> [Segmentation] --> [Feature Extraction] --> [Model Prediction] --> [Result + Remedies]
- User-Friendly UI: Designed with Dash and Bootstrap for a seamless experience.
- Accurate Predictions: Leveraging advanced CNNs for high accuracy (>85%).
- Disease Remedies: Provides actionable steps for detected conditions.
We welcome contributions to make MediScan even better!
Feel free to fork the repo, make changes, and create pull requests.
This project is licensed under the MIT License.
Thanks to the amazing team at MediScan for making this project a reality. 🙌
Stay tuned for more updates and features! 🩺✨