This project utilizes a deep learning model trained with Google Teachable Machine to classify images into four categories: MRI, CT, X-Ray, and non-medical images. The trained model is integrated into a Streamlit web application to provide an interactive interface for users to upload and classify images.
Wanna try the model ? https://medicalimageclassification.streamlit.app/
Medical Image Classification Web App is a tool designed to make the process of classifying medical images straightforward and accessible. By leveraging Google Teachable Machine and Streamlit, the project combines an intuitive training process with an easy-to-use web interface.
Note: Python version 3.9.0 is required to deploy on streamlit
- Google Teachable Machine: Train a deep learning model with a user-friendly interface.
- Deep Learning Model: Exported as a
.h5
file for easy integration. - Streamlit Web App: Interactive platform for image upload and classification.
- Image Categories: Classify images into MRI, CT, X-Ray, and non-medical images.
- User-Friendly: Accessible for both medical professionals and laypersons.
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Clone the Repository:
git clone https://github.com/mohitmahajan095/Medical_Image_Classification.git cd Medical_Image_Classification
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Install Dependencies:
pip install -r requirements.txt
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Download the Model: Place the
.h5
file trained using Google Teachable Machine into the project directory.
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Run the Web App:
streamlit run app.py
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Upload and Classify Images:
- Open the web app in your browser.
- Use the upload button to select an image.
- The app will display the classification result.
This project is licensed under the MIT License. See the LICENSE file for details.