Melanoma Risks Detection Using Ensemble Random Forest Model

Skin cancer continues to pose significant challenges to medical professionals and researchers. Among its various forms, melanoma skin cancer stands out as a particular concerning malignancy. Melanoma primarily originates in melanocytes, the skin cells responsible for pigment production. Early detection of melanoma is crucial, given its potential lethality. In this research paper, we present a comprehensive analysis of melanoma detection using the Random Forest (RF) algorithm on the PH2 dataset. It is very essential to detect melanoma in earlier stages because of its fatal nature and this research tries to improve the accuracy and efficiency of the skin cancer diagnosis for better patient outcomes. The methodology used in this research includes collection of data, preprocessing it, segmentation of the processed data, feature extraction, RF classification, and analysis of result. The RF model we used gave an accuracy of 96.5% with low computational time delay of 3.50 s hence making it an effective tool for melanoma diagnosis. Overall, the research highlights the significance of early and accurate melanoma detection along with feasibility and practicality of Random Forest model for better patient outcomes.