Extravasation Screening and Severity Prediction from Skin Lesion Image using Deep Neural Networks

Munthuli, Adirek; Intanai, J; Tossanuch, P; Pooprasert, Pakinee; Ingpochai, P; Boonyasatian, S; Kittithammo, K; Thammarach, P.; Boonmak, T; Khaengthanyakan, S.; Yaemsuk, A.; Vanichvarodom, P.; Phienphanich, Phongphan; Pongcharoen, P; Sakonlaya, D; Sitthiwatthanawong, P; Wetchawalit, S; Chakkavittumrong, P; Thongthawee, B; Pathomjaruwat, T; Tantibundhit, Charturong

doi:10.1109/embc48229.2022.9871115

Cited by 1 publication

(1 citation statement)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this study, we chose four deep learning models for comparison purposes, including three traditional convolutional neural networks (i.e., MobileNetv2, ResNet50, and ) and a novel network (i.e., Swin Transformer). In previous studies, MobileNet was able to work on lightweight computing devices and had high accuracy in classification of skin disease images [24]; ResNet50 showed superior performance for segmentation and classification in multiple skin lesions diagnostics [25]; DenseNet121 was also used to segment skin and lesion [26]; and Swin Transformer was a novel fine-grained recognition framework and showed more powerful and robust features in medical image segmentation [27]. Therefore, we selected these mainstream and high-performance deep learning networks to explore their performance in melasma diagnosis task.…”

Section: Discussionmentioning

confidence: 99%

An Intelligent Diagnostic Model for Melasma Based on Deep Learning and Multimode Image Input

Liu

Chen

Xue

et al. 2022

Dermatol Ther (Heidelb)

View full text Add to dashboard Cite

Introduction:The diagnosis of melasma is often based on the naked-eye judgment of physicians. However, this is a challenge for inexperienced physicians and non-professionals, and incorrect treatment might have serious consequences. Therefore, it is important to develop an accurate method for melasma diagnosis. The objective of this study is to develop and validate an intelligent diagnostic system based on deep learning for melasma images. Methods: A total of 8010 images in the VISIA system, comprising 4005 images of patients with melasma and 4005 images of patients without melasma, were collected for training and testing. Inspired by four high-performance structures (i.e., DenseNet, ResNet, Swin Transformer, and MobileNet), the performances of deep learning models in melasma and nonmelasma binary classifiers were evaluated. Furthermore, considering that there were five modes of images for each shot in VISIA, we fused these modes via multichannel image input in different combinations to explore whether multimode images could improve network performance. Results: The proposed network based on Den-seNet121 achieved the best performance with an accuracy of 93.68% and an area under the curve (AUC) of 97.86% on the test set for the melasma classifier. The results of the Gradientweighted Class Activation Mapping showed that it was interpretable. In further experiments, for the five modes of the VISIA system,

show abstract