X-Ray Analyzer

X-Ray Analyzer is a web application that implements a deep learning model for generating results after analysing X-ray images. This repository contains the code for the website, which is built using Flask for the backend, HTML, CSS, and JavaScript for the frontend.

Table of Contents

• Introduction
• Requirements
• Installation
• Usage
• Dataset
• Model Architecture
• Website Structure
• Contributions

Introduction

The X-Ray Analyzer provides an interactive platform for users to upload X-ray images and generate description for those images using a deep learning model. The model combines computer vision and natural language processing techniques to generate meaningful textual descriptions that accurately represent the content of the X-ray images. This can be useful in medical diagnosis, research, and educational applications.

Requirements

To run the website, you need the following dependencies:

• Python (3.6 or higher)
• Flask (1.0 or higher)
• TensorFlow (2.0 or higher)
• NumPy
• Pandas

Installation

1. Clone the repository: git clone (https://github.com/Tusharv0607/X-Ray_Analyzer.git)
2. Navigate to the project directory
3. Install the required dependencies: pip install -r requirements.txt

Usage

1. Start the Flask server: python app.py
2. Open your web browser and go to http://localhost:5000.
3. Upload an X-ray image using the provided interface.
4. Click the "Get Results" button to process the image and display the generated caption.
5. Repeat the process for additional images.

Dataset

The deep learning model used by the website is trained on a dataset of X-ray images along with their corresponding captions for training.

Model Architecture

The deep learning model used in this project is based on a combination of convolutional neural networks (CNN) and LSTM based recurrent neural networks (RNN). The CNN is responsible for extracting image features, while the LSTM generates the textual captions based on the extracted features. The architecture consists of an image encoder and a caption decoder. The image encoder is typically a pre-trained CNN (VGG16) that is used to encode the X-ray images into a fixed-length vector of features. The caption decoder is an RNN (LSTM) that generates results word by word based on the encoded image features.

Website Structure

The website structure is as follows:

• The app.py file contains the Flask application code, including the routes for handling image uploads and generating captions.
• The templates directory contains the HTML templates for the website pages.
• The static directory contains the CSS and JavaScript files used for styling and interactivity.

Contributions

Contributions to the AutoImageDescriptor Website are welcome. If you find any issues or have suggestions for improvements, please open an