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• Promote Early Intervention: Detect skin issues early with AI-powered analysis.
• Enable Comprehensive Skin Wellness: Track skin health over time and manage UV exposure.
• Improve Healthcare Accessibility: Offer telemedicine and hospital recommendations for remote users.

• EfficientNetB0 model analyzes skin images for condition detection.

• Personalized guidance based on user responses.

• Text-to-speech and speech-to-text for elderly users.

• Seamlessly connects users with nearby hospitals and healthcare professionals via Jitsi Meet.

• Provides real-time UV index data and personalized warnings for sun protection.

For training the AI-powered skin condition analysis model, we use the HAM10000 dataset, which consists of three key files:

1. HAM10000_images_part_1 and HAM10000_images_part_2:
   These files contain a large collection of skin lesion images. The images represent various dermatological conditions and are used to train the model for skin condition identification.

2. HAM10000_metadata:
   This file contains metadata corresponding to the images in the above parts. The metadata includes the following information:

   • Skin Condition: Labels for different types of skin conditions (e.g., melanoma, basal cell carcinoma, etc.).
   • Age: The age of the individual who provided the skin image.
   • Gender: The gender of the individual.
   • Location: The geographical location where the image was taken or where the individual resides.

Due to the size of the dataset, these files are not hosted on GitHub. However, you can access the dataset by visiting HAM10000 dataset repository. Please ensure proper usage and citation when working with the dataset.

This data is used to train the EfficientNetB0 model to detect skin conditions accurately and offer personalized insights for users.

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