A Novel Convolutional Neural Network for the Diagnosis and Classification of Rosacea: Usability Study

Abstract

Background Rosacea is a chronic inflammatory disease with variable clinical presentations, including transient flushing, fixed erythema, papules, pustules, and phymatous changes on the central face. Owing to the diversity in the clinical manifestations of rosacea, the lack of objective biochemical examinations, and nonspecificity in histopathological findings, accurate identification of rosacea is a big challenge. Artificial intelligence has emerged as a potential tool in the identification and evaluation of some skin diseases such as melanoma, basal cell carcinoma, and psoriasis. Objective The objective of our study was to utilize a convolutional neural network (CNN) to differentiate the clinical photos of patients with rosacea (taken from 3 different angles) from those of patients with other skin diseases such as acne, seborrheic dermatitis, and eczema that could be easily confused with rosacea. Methods In this study, 24,736 photos comprising of 18,647 photos of patients with rosacea and 6089 photos of patients with other skin diseases such as acne, facial seborrheic dermatitis, and eczema were included and analyzed by our CNN model based on ResNet-50. Results The CNN in our study achieved an overall accuracy and precision of 0.914 and 0.898, with an area under the receiver operating characteristic curve of 0.972 for the detection of rosacea. The accuracy of classifying 3 subtypes of rosacea, that is, erythematotelangiectatic rosacea, papulopustular rosacea, and phymatous rosacea was 83.9%, 74.3%, and 80.0%, respectively. Moreover, the accuracy and precision of our CNN to distinguish rosacea from acne reached 0.931 and 0.893, respectively. For the differentiation between rosacea, seborrheic dermatitis, and eczema, the overall accuracy of our CNN was 0.757 and the precision was 0.667. Finally, by comparing the CNN diagnosis with the diagnoses by dermatologists of different expertise levels, we found that our CNN system is capable of identifying rosacea with a performance superior to that of resident doctors or attending physicians and comparable to that of experienced dermatologists. Conclusions The findings of our study showed that by assessing clinical images, the CNN system in our study could identify rosacea with accuracy and precision comparable to that of an experienced dermatologist.

Machine Learning and Deep Learning Methods for Skin Lesion Classification and Diagnosis: A Systematic Review

“…Many CAD systems have been identified using different border detection, extraction, selection, and classification algorithms. Some studies [51][52][53][54][55][56][57][58] have proposed the study and analysis of image processing techniques to diagnose skin cancer; moreover, they compared Artificial Intelligence (AI) and CAD system performances against the diagnostic accuracy of experienced dermatologists. However, further work is required to define and reduce ambiguity in automated decision support systems to enhance diagnostic accuracy.…”

Skin Disease Analysis With Limited Data in Particular Rosacea: A Review and Recommended Framework

“…Most of the works which have shown great results using deep learning have used at least nearly 10,000 images. A few studies conducted by Thomsen et al [31], Zhao et al [32], Wu et al [33] and Zhu et al [34], employ a significant quantity of data. However, the datasets used in these studies are entirely confidential.…”

“…Zhao et al [32] carried out a study on three subtypes of rosacea lesions i.e. Erythematotelangiectatic rosacea (ETR), papulopustular rosacea (PPR), and phymatous rosacea (PhR).…”

A novel multi-layer perceptron model for assessing the diagnostic value of non-invasive imaging instruments for rosacea

“…The correct diagnosis of rosacea depends largely on the clinician’s subjective perception and experience ( Tan et al, 2017b ; Thiboutot et al, 2020 ). In recent years, the convolutional neural network has been utilized to objectively assess and classify rosacea based on the clinical photos of rosacea patients ( Binol et al, 2020 ; Zhao et al, 2021 ). These networks are established based on a single type of image and require a huge amount of photos for training the network.…”