• Shwetambari Borade
• Dhananjay Kalbande

In a given picture, human skin disease identification entails recognizing the skin type and the areas with the affected component of the skin. Skin ailments are frequently detected using colour pixels, textures, and edges. These characteristics are constant and quick to process. In this research, a new Skin Condition Detection Model is proposed. The RGB (Red, Green, Blue) colour model, the edges of the image, and the roughness of the skin are the three key factors used to diagnose skin disorders. The proposed model's aim is to strengthen the present skin disease detection system even while working on various skin disorders. This model employs machine learning techniques along with XGBoost in addition to the previous parameters.

"Framework for Cosmetic skin disease detection using Machine Learning and Extreme Gradient Boosting", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 6, page no.h820-h824, June-2022, Available :http://www.jetir.org/papers/JETIR2206793.pdf

"Framework for Cosmetic skin disease detection using Machine Learning and Extreme Gradient Boosting", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 6, page no. pph820-h824, June-2022, Available at : http://www.jetir.org/papers/JETIR2206793.pdf