Skin Disease Detection Using Machine Learning and Image Processing

EasyChair Preprint no. 7995

Abstract

Skin illness is the most common sickness on the planet. When diagnosing skin disorders, dermatologists must have a high level of competence and accuracy, so a computer-aided skin disease diagnosis model is presented as a more objective and reliable solution. Much research has been carried out to aid in the diagnosis of skin diseases such as skin cancer and tumors. However, due to factors such as low contrast between lesions and skin, visual similarity between the Disease and nonDisease parts, and so on, correct disease recognition is highly difficult. This study aims to detect skin disease from a skin image and analyze it by applying a filter to remove noise and unwanted items, changing the image to grey to aid processing, and extracting useful information. This can be used to indicate emergency preparedness and provide proof of any type of skin disease.

Keyphrases: Accuracy, Dermatologists, Disease recognition, emergency preparedness, Skin illness

@Booklet{EasyChair:7995,
  author = {Gunjan Chauhan and Swapnaja Ubale},
  title = {Skin Disease Detection Using Machine Learning and Image Processing},
  howpublished = {EasyChair Preprint no. 7995},

  year = {EasyChair, 2022}}