Inspiration
In recent years, skin diseases have become a growing concern worldwide, affecting millions of people. Early detection and diagnosis can significantly improve treatment outcomes and quality of life for patients. Inspired by the potential of artificial intelligence in healthcare, we aimed to create a tool that leverages AI to assist in the early detection of skin diseases. Our goal is to provide a quick, reliable, and accessible solution for both healthcare providers and patients.
What it does
The AI Skin Disease Detector is a web-based application that allows users to upload images of skin lesions. The application analyzes the images using a pre-trained AI model to predict the type of skin disease.
How we built it
Frontend: The frontend is built using React, providing a user-friendly interface for uploading images and displaying results. It includes components for image upload, result display, and additional information about detected diseases. Backend: The backend is developed with Flask, a Python web framework. It handles image processing, prediction, and communication with the AI model. AI Model: We initially faced challenges with the performance of our custom model. To address this, we utilized a pre-trained model, which significantly improved the speed and accuracy of our predictions.
Challenges we ran into
Model Performance: Our initial model was running extremely slow, making it impractical for real-time predictions. After several iterations and optimizations, we decided to switch to a pre-trained model, which greatly improved performance. Integration: Connecting the frontend and backend posed several challenges, especially with ensuring smooth data flow and handling CORS policies. Debugging and resolving these issues required extensive testing and collaboration.
Accomplishments that we're proud of
Successfully integrating a pre-trained AI model to achieve fast and accurate predictions. Developing a cohesive and user-friendly interface that provides valuable information to users. Overcoming technical challenges in connecting the frontend and backend, ensuring a seamless user experience.
What we learned
The importance of using pre-trained models to save time and improve performance. Advanced techniques in integrating different technology stacks, particularly in web development and API management. The value of teamwork and persistence in overcoming technical obstacles and achieving project goals.
What's next for Skin Disease Detector
Enhanced Model Accuracy: Continuously improve the model’s accuracy by incorporating more diverse datasets and advanced AI techniques. Mobile Application: Develop a mobile version of the application to increase accessibility and usability. Expanded Database: Include more skin diseases and expand the treatment database to provide comprehensive information. User Feedback Integration: Implement features for user feedback to continuously refine and improve the application based on real-world usage.
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