HealthFusion has identified a critical business problem, which is the lack of accessibility and timely detection of multiple diseases. The traditional approach of detecting diseases is time-consuming, expensive, and not accessible to everyone, especially in remote areas. This problem can lead to delayed diagnosis and treatment, which can have serious consequences for patients.
The proposed solution, HealthFusion, is novel and practical as it offers a comprehensive solution to detect multiple diseases using the power of AI. HealthFusion is a user-friendly app that can be accessed from the comfort of homes, making it accessible to everyone. The use of advanced technologies such as Convolutional Neural Networks, Random Forest, and XGBoost allows for accurate and timely detection of diseases, leading to better patient outcomes.
*Python - Jupyter Notebook (TensorFlow for Model Training) *Flask Web Framework (for Backend) *Google Firebase (for Real-Time Storage and Firestore Database) *HTML, CSS, and JS (for Frontend) *Ai Powered chatbot Support (Openai / Gemini Advance API)
*Python v3.8.10
The dataset for the project was gathered from multiple sources. Two of them are as follows:
Chest X-ray images (1000 images) were obtained from: https://github.com/ieee8023/covid-chestxray-dataset CT Scan images (750 images) were obtained from: https://github.com/UCSD-AI4H/COVID-CT/tree/master/Data-split 80% of the images were used for training the models and the remaining 20% for testing.
*Ai chatbot
HealthFusion Screencast 5minutes Pitch English
https://www.youtube.com/embed/D8oOvi2zO3s
What's next for HealthFusion
HealthFusion has ambitious plans for the future. The platform aims to expand its disease detection capabilities by adding more diseases that can be detected using X-ray scans or by inputting simple numbers. The platform also plans to provide patients with information about the precautions they need to take and how they can cure themselves if they test positive for a particular disease. Additionally, HealthFusion plans to store detection records to help doctors monitor patients' health and track their progress. The team also plans to refine the platform's revenue model as they gather feedback from users and continue to grow and expand their business. Overall, HealthFusion aims to continue disrupting the healthcare industry by providing an accessible and convenient medical solution that leverages the power of technology.
On next update--------->
Implemting GPT for gather and implementing real time prediction data.
Implementing a Database to Store User Data.
Implementing a Api for Faster User data managment with RapidApi.
Project Overview Full details / Presentation / Brochure / Datasheet pdf files
Project Overview link
Project Presentation link
Project Brochure link
Project Datasheet link
Built With
flask
html
machine-learning
python
How to run this project
1.Clone the Repo
2.Create a conda environment and install the required libraries,
conda create -n healthfusion python=3.9
conda activate healthfusion
pip install opencv-python numpy tensorflow scikit-learn imutils flask xgboost
3. When you have successfully created the environment, installed the required libraries, and activated it, simply run the following command in the terminal.
flask run
Lasantha Karunarathne link
Googlex Technologies link
contact me :- lkkarunarathne143@gmail.com
