DermaVision

Inspiration

The main inspiration for DermaVision came from one of our team member's close relatives that was affected by a skin disease. The reason why this project is so personal to our team is because this could've been prevented with early detection of the disease, especially because their relative resides in India, where cutting-edge medical technology and expertise is in short supply.

Features

• Image Upload: Users can easily upload an image of their skin condition.
• AI-Powered Diagnosis: The tool uses a trained CNN model to analyze the image and predict potential skin diseases.
• User-Friendly Interface: Simple and intuitive design for seamless user experience.

How It Works

1. Image Upload: Users upload a high-quality image of the affected skin area.
2. CNN Analysis: The uploaded image is processed through a trained CNN model that detects patterns and features.
3. Disease Identification: The model compares the image with known skin disease patterns and provides a diagnosis, including probabilities for the most likely conditions.

Technologies Used

• Convolutional Neural Networks (CNNs): The core technology behind DermaVision, trained on a large dataset of labeled skin condition images.
• Image Processing: Pre-processing of images to enhance quality and normalize for analysis.
• REST API: Allows users to upload images and receive diagnosis through HTTP requests.
• Frontend: A user-friendly interface for image upload and result presentation.

Challenges Faced

With DermaVision, the main challenges our team faced were regarding the data-science aspect of the solution. This included everything from preventing overfitting given the high dimensionality of image data and model complexity to ensuring the model generalizes well to new, real-world images not seen during training

Built With

• cnn
• css3
• flask
• git
• github
• html5
• javascript
• keras
• machine-learning
• numpy
• pandas
• python
• pytorch
• react
• scikit-learn
• tensorflow