Medical Image Classification Web App

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/

Table of Contents

• Introduction
• Features
• Installation
• Usage
• Model Training
• License

Introduction

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

Features

• 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.

Installation

1. Clone the Repository:

   git clone https://github.com/mohitmahajan095/Medical_Image_Classification.git
   cd Medical_Image_Classification

2. Install Dependencies:

   pip install -r requirements.txt

3. Download the Model: Place the .h5 file trained using Google Teachable Machine into the project directory.

Usage

1. Run the Web App:

   streamlit run app.py

2. 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.

License

This project is licensed under the MIT License. See the LICENSE file for details.