AI Skin Diseases Detector

Empowering Your Skin Health with AI Precision

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AI Skin Disease Classification Application

Inspiration

I observed that many people suffer from skin diseases, which are often difficult to diagnose accurately. Incorrect diagnoses are common, leading to ineffective treatments. This inspired me to develop an AI-driven solution to assist in the detection and classification of skin diseases.

What it Does

This AI model acts as a virtual doctor, helping patients identify the type of skin disease they may be suffering from by analyzing uploaded images of their skin conditions.

How We Built It

  • Backend: We used TensorFlow Serving and FastAPI to create a flexible and efficient server.
  • Frontend: The user interface was developed with React. It allows users to upload images, receive predictions, and get feedback in an intuitive and user-friendly format.
  • Machine Learning: The classification model was trained using a dataset of labeled skin images. TensorFlow was utilized to train and save the model for deployment.

Challenges We Ran Into

  • Data: Acquiring a large and high-quality dataset was one of the biggest challenges, as it is essential for building an effective AI model.
  • Training the Model: Training the model on my laptop caused performance issues, necessitating the use of a borrowed laptop and a subscription to Google Colab for sufficient computational resources.
  • Integration: Integrating the machine learning model with both the backend and frontend while maintaining performance and scalability was a complex task.

Accomplishments That We're Proud Of

  • High Accuracy: The model achieved high classification accuracy on the test set, demonstrating its effectiveness in identifying various skin conditions.
  • Seamless Integration: Successfully integrated the machine learning model with a responsive and intuitive user interface.

What We Learned

  • Importance of Data: High-quality and diverse datasets are crucial for training effective models.

What's Next for AI Skin Diseases Detector

  • Expand Dataset: Continue to expand and diversify the dataset to improve model accuracy and robustness.
  • Mobile App: Developing a mobile app version to enhance accessibility and usability for users on the go.

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