A novel artificial intelligence-based predictive analytics technique to detect skin cancer.

ABSTRACT: One of the leading causes of death among people around the world is skin cancer. It is critical to identify and classify skin cancer early to assist patients in taking the right course of action. Additionally, melanoma, one of the main skin cancer illnesses, is curable when detected and treated at an early stage. More than 75% of fatalities worldwide are related to skin cancer. A novel Artificial Golden Eagle-based Random Forest (AGEbRF) is created in this study to predict skin cancer cells at an early stage. Dermoscopic images are used in this instance as the dataset for the system's training. Additionally, the dermoscopic image information is processed using the established AGEbRF function to identify and segment the skin cancer-affected area. Additionally, this approach is simulated usi

SUBMITTER: Balaji P 

PROVIDER: S-EPMC10280503 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature