Diagnostic Assistant

A machine-learning-powered web application for diagnosing medical conditions based on image inputs.

Overview

The Diagnostic Assistant is a Flask-based web application that utilizes a deep learning model to analyze medical images and provide diagnostic insights. The system allows users to upload medical images, process them through a trained model, and receive an evaluation. The results are displayed on an interactive UI with additional options for reviewing and printing the diagnosis.

Features

• Image Upload: Users can upload medical images for diagnosis.
• Deep Learning Model: The application employs a pre-trained Keras model for image classification.
• Flask Web Server: Manages image processing, model inference, and result rendering.
• Interactive UI: Built with HTML, CSS, and JavaScript to provide a smooth user experience.
• Review and Print: Users can review the diagnosis and print the results for documentation.

Project Structure

Diagnostic_Assistant/
│── backend/  
│   │── app.py (Main Flask application)  
│   │── app1.py (Additional backend functionalities)  
│   │── requirements.txt (Dependencies for the project)  
│── static/  
│   │── script.js (Frontend logic)  
│   │── styles.css (Styling for UI)  
│── templates/  
│   │── index.html (Main UI)  
│   │── review.html (Review page)  
│   │── print.html (Print diagnosis)  
│── uploads/ (Uploaded images)  
│── venv/ (Virtual environment)  
│── malaria_detect_model.keras (Pre-trained ML model)  

Installation & Setup

1. Clone the Repository

git clone https://github.com/lakshug23/diagnostic_assistant.git
cd Diagnostic_Assistant

2. Set Up Virtual Environment

python3 -m venv venv
source venv/bin/activate   # On Windows use: venv\Scripts\activate

3. Install Dependencies

pip install -r requirements.txt

4. Run the Application

python app.py

The Flask server will start at http://127.0.0.1:5000/. Open this URL in your browser to access the Diagnostic Assistant.

Usage

1. Open the web app in your browser.
2. Upload a medical image (e.g., malaria-infected blood sample).
3. Click on Diagnose to analyze the image.
4. View the results and proceed to Review or Print the diagnosis.

Tech Stack

• Backend: Python, Flask
• Frontend: HTML, CSS, JavaScript
• Machine Learning: Keras, TensorFlow
• Deployment: Local server via Flask

Future Improvements

• Implement a more advanced ML model for better accuracy.
• Add user authentication and history tracking.
• Enable cloud-based model inference for faster processing.

Screenshots

1. Home Page

2. Diagnosis Page

3. Prescription Page

4. Print Page

Demo Video

Click here to watch the demo