This dataset combines images from multiple sources to provide a comprehensive collection for skin disease classification, aiding in research, diagnostic model development, and practical AI applications in dermatology. It includes nine distinct disease categories, each representing specific skin conditions commonly encountered in clinical settings. The categories are:

1. Actinic Keratosis (AK/ACK) – Precancerous lesions caused by sun exposure, often appearing as rough, scaly patches.
2. Basal Cell Carcinoma (BCC) – A common type of skin cancer, typically manifesting as small, pearly bumps or lesions.
3. Dermatofibroma (DF) – Benign skin growths that usually appear as small, firm nodules.
4. Melanoma (MEL) – A severe form of skin cancer that can appear as a new mole or a change in an existing mole.
5. Melanocytic Nevi (NV) – Commonly known as moles, these are generally benign pigmented skin lesions.
6. Squamous Cell Carcinoma (SCC) – Another form of skin cancer, often found on sun-exposed areas, presenting as rough, red patches.
7. Vascular Lesion (VASC) – Lesions related to blood vessels, such as hemangiomas and vascular malformations.
8. Seborrheic Keratoses (SEK) – Benign, wart-like growths often seen in older adults.
9. Acne – A common skin condition characterized by pimples, cysts, and blackheads, often affecting the face and back.

Dataset Sources
This dataset draws from various well-regarded sources, providing a wide range of skin conditions across diverse demographics, skin tones, and presentation variations. The primary source is the ISIC 2019 dataset, supplemented by images from the following collections:

1. ISIC 2019
2. Mendeley
3. BCN
4. PAD-UFES-20 (Mendeley)
5. MED-NODE Dataset
6. SD-198
7. Light Field
8. Dermnet
9. Kaggle Face Skin Disease Dataset
10. Kaggle Acne Dataset
11. Kaggle Skin Disease Classification Dataset
12. Kaggle Seborrheic Keratoses (SEK)
13. Kaggle 20 Skin Diseases Dataset
14. Kaggle Skin Diseases Image Dataset
15. Roboflow Acne Computer Vision Project