From literature review and field facts, it has been found that there is a recurring process in hospitals of reading disease detection results using X-rays. Meanwhile, the demand for chest disease examinations is increasing. It is often found that doctors make reading errors, and even small hospitals without a resident doctor struggle to produce accurate and timely readings. However, we know that X-ray results are an important indicator of the emergence of diseases. Cancer is a serious disease that is often difficult to detect in its early stages, resulting in high mortality rates and a significant burden on society's health. Field facts show the existence of small hospitals with limited medical facilities and a shortage of doctors. Moreover, even though doctors are equipped with competencies to read x-ray results well, factors such as fatigue and lack of concentration often result in inaccurate readings. Furthermore, some x-ray image qualities are poor, making reading even more challenging. From the perspective of medical students, it would be very interesting if they could play with an application that could directly classify results from the x-ray images that are inputted.
These are the screenshots of our product. First, on the left displays the home view and there is a guide for using the application. Second from the left, displays the user's profile page. The next image shows the detection results from the x-ray images that have been uploaded. Finally, displays articles to add insight to users.
For more detail, please check this repository.
This will show you how the app works. You can direcly access the link demo X-Detect applications using by this link
Our application is based on Android OS. In order to use our application, you will need an Android device that can runs at least Android OS Version 6.0 (Marshmallow). You can download our application (.apk) from the link down below.
https://drive.google.com/file/d/1xBgJDhGKiLfhRGLtUbAiLrehN9WIsIWq/view?usp=sharing
The prototype design of this application is based on Figma. If you want to see the prototype design of this application, you can access it using the link provided below.
- Chest X-Ray Disease Detection ()
- Treatment Advice
- Article
- Authentication
- Login and Register
- Profile and Edit Profile
- History (failed to deploy)
Mobile Application: Retrofit, Android SDk, Android Support Library, CameraX, Hdodenhof's CircleImageView, Glide.
For more detail, please check this repository.
Backend Server: Node, NPM, JavaScript, Cloud Run, Express JS, Firebase, Tensorflow JS, Flask, Python, Postman.
For more detail, please check this main API repository and this prediction repository.
Machine Learning Model: Jupyter Notebook, Tensorflow, Tensorflow JS, NumPy, Matplotlib, Pandas, OpenCV, Skimage, sciPy, os
For more detail, please check this repository.
Bangkit 2023 Capstone Team C23-PC603
- Dzaky Abdillah Salafy - Machine Learning - M121DKX4633
- Ahmad Habib Rizqi - Machine Learning - M260DSX2594
- Akhmad Nur Fadhil - Machine Learning - M194DKX4708
- Farhan Aly Hasbi - Cloud Computing - C038DSX0604
- Rizal Nawang Pradana - Cloud Computing - C254DSX0785
- Zainatul Sirti - Mobile Development - A304DSY1205
