Design and validation of a new machine-learning-based diagnostic tool for the differentiation of dermatoscopic skin cancer images.

ABSTRACT:

Background

Skin cancer is the most common cancer in the United States. Current estimates are that one in five Americans will develop skin cancer in their lifetime. A skin cancer diagnosis is challenging for dermatologists requiring a biopsy from the lesion and histopathological examinations. In this article, we used the HAM10000 dataset to develop a web application that classifies skin cancer lesions.

Method

This article presents a methodological approach that utilizes dermoscopy images from the HAM10000 dataset, a collection of 10015 dermatoscopic images collected over 20 years from two different sites, to improve the diagnosis of pigmented skin lesions. The study design involves image pre-processing, which includes labelling, resizing, and data augmentation techniques to i

SUBMITTER: Tajerian A 

PROVIDER: S-EPMC10104315 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature