Asclepius

Asclepius is an Android application designed to assist in detecting cancer through image analysis. The app utilizes a machine learning model (TensorFlow Lite) to classify images as cancerous or non-cancerous. It also features a news page with the latest articles related to cancer research and treatments. All analysis history is stored in a dedicated history page for easy access and reference.

Screenshots

Table of Contents

• Features
• Technologies Used
• Architecture
• Machine Learning Model
• Getting Started
  • Prerequisites
  • Installation
• App Structure
• Usage
• Contributing
• License
• Contact

Features

• Cancer Detection: Upload a picture, and the app will use TensorFlow Lite to analyze the image and detect if cancer is present.
• News Feed: Get the latest news and articles related to cancer, research, treatments, and health updates.
• History: Stores past analyses, allowing users to review their detection history.
• User-friendly UI: Simple navigation and interaction, focusing on accessibility and ease of use.

Technologies Used

• Language: Kotlin
• Machine Learning: TensorFlow Lite
• Data Binding: LiveData, ViewModel
• Networking: Retrofit for fetching news data
• Local Storage: Room Database for storing analysis history
• UI: RecyclerView, Material Components, View Binding
• Others: AndroidX, Navigation Component

Architecture

This app follows the MVVM (Model-View-ViewModel) architecture pattern, which helps in maintaining a clean separation of concerns, easy testing, and better organization of code:

• Model: Handles the image classification using TensorFlow Lite and stores history data using Room Database.
• View: Displays the results, news articles, and history in a clean and intuitive user interface.
• ViewModel: Manages the data and logic, coordinating between the View and Model.

Machine Learning Model

• The cancer detection model is implemented using TensorFlow Lite.
• The model is pre-trained on medical image data to classify cancerous and non-cancerous images.
• Image Preprocessing: The images are processed to fit the input size of the model for accurate predictions.

Getting Started

To get a local copy of the project up and running on your machine, follow these steps:

Prerequisites

Ensure you have the following software installed:

• Android Studio (latest stable version recommended)
• Git (for cloning the repository)
• TensorFlow Lite Model (integrated into the project)
• News API's API Key

Installation

1. Clone the repository: Open a terminal and run the following command to clone the repository: git clone https://github.com/zyrridian/asclepius.git

2. Open the project in Android Studio:

   • Open Android Studio.
   • Select Open an existing Android Studio project.
   • Navigate to the cloned repository and select it.

3. Build the project:

   • Create a new ApiKey.kt object class and use your own API Key. Or you can put it somewhere else.
   • Android Studio will automatically build the project.
   • If the build does not start automatically, select Build > Make Project from the menu.

4. Run the app:

   • Connect your Android device or use an emulator.
   • Click the green play button to run the app on the device.

App Structure

• Home Page: The main interface where users can select or take an image for cancer detection.
• Detection Page: Once an image is selected, the TensorFlow Lite model processes the image and displays the detection result (Cancerous / Non-Cancerous).
• News Page: Displays the latest cancer-related news fetched via Retrofit. Articles include titles, summaries, and links to full articles.
• History Page: Stores all past image analysis results with timestamps, allowing users to review their history.

Usage

Once the app is running, you can:

• Detect Cancer: Upload or take a picture of a suspect area, and the TensorFlow Lite model will analyze it to detect cancer.
• Read News: Stay updated with the latest news on cancer research, treatments, and health.
• Review History: View previous analysis results on the history page, where results are stored locally using Room Database.

Image Detection Flow:

• Image Capture/Upload: The user can take a photo or upload one from their gallery.
• Image Processing: The image is resized and preprocessed for the TensorFlow Lite model.
• Result Display: The app provides a clear result (Cancerous / Non-Cancerous) along with an option to save it in history.

Contributing

Contributions are welcome! If you would like to contribute to the project, please follow these steps:

1. Fork the repository.
2. Create a new branch (git checkout -b feature/your-feature).
3. Make your changes.
4. Commit your changes (git commit -m 'Add some feature').
5. Push to the branch (git push origin feature/your-feature).
6. Create a pull request. Please ensure your code follows the existing code style and includes appropriate documentation.

License

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

Contact

• Author: Rezky Aditia Fauzan
• Email: [email protected]
• GitHub: https://github.com/zyrridian