A deep learning-based approach toward differentiating scalp psoriasis and seborrheic dermatitis from dermoscopic images.

ABSTRACT:

Objectives

This study aims to develop a new diagnostic method for discriminating scalp psoriasis and seborrheic dermatitis based on a deep learning (DL) model, which uses the dermatoscopic image as input and achieved higher accuracy than dermatologists trained with dermoscopy.

Methods

A total of 1,358 pictures (obtained from 617 patients) with pathological and diagnostic confirmed skin diseases (508 psoriases, 850 seborrheic dermatitides) were randomly allocated into the training, validation, and testing datasets (1,088/134/136) in this study. A DL model concerning dermatoscopic images was established using the transfer learning technique and trained for diagnosing two diseases.

Results

The developed DL model exhibits good sensitivity, specificity, and Area Under Curv

SUBMITTER: Yu Z 

PROVIDER: S-EPMC9669613 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature