This dataset is a curated collection of high-quality images depicting various common skin conditions and rashes. It was developed by combining resources from reputable dermatological image libraries such as DermNet NZ, Kaggle's DermNet collection, and the IEEE DataPort. The dataset is structured to facilitate easy access to images categorized by skin color (Black, Brown, Fair) and condition type (Dermatitis, Eczema, Ringworm), making it an ideal resource for training and testing machine learning models in medical imaging.

Key Features:

• Diverse Conditions: Includes multiple types of skin conditions like dermatitis, eczema, and ringworm.
• Skin Tone Variation: Categorized images based on different skin tones to promote inclusivity and accuracy in diagnosis algorithms.
• Ready for ML: Organized in a machine-learning-friendly format to streamline the development of diagnostic models.
• Educational Value: A valuable resource for educational purposes in dermatology and machine learning fields.

Potential Uses:

• Medical Image Recognition: Train deep learning models to recognize and diagnose various skin conditions.
• Algorithm Testing: Test the efficacy of image processing algorithms in differentiating and categorizing skin conditions.
• Educational Tool: Serve as a visual aid for educational institutions teaching dermatology or machine learning applications in medicine.