Skin-tect

Skin-tect aims to provide tools and education about how artificial intelligence (AI) can be used to detect skin cancer, while raising awareness about current bias and issues with existing skin lesion detection AI, and working to offer healthcare providers with easy tools to reduce bias in the data and improve accuracy of predictions. Our website is powered by an AI that we created. The model is built on tensorflow and is cross-trained from the Xception model, which has over 22 million parameters. Using the imagenet weights as a base, the model was further trained on the HAM10000 dataset – a dataset of over 10,000 images of different types of skin lesions, including several cancerous and benign classifications. Our model achieved over 87% accuracy in classifying the lesions and with more work and time, this number could improve significantly. We implemented functionality for an everyday user to be able to use our AI to detect potentially dangerous skin lesions, while making sure to provide additional healthcare and technological information. We have also set up a page that will backed by a database, so that healthcare providers can input their medical licensing information to be verified before creating an account to upload images of skin lesions that they diagnose to expand our dataset, reduce bias in AI, and continue to improve the accuracy of the AI’s analysis.