AI-based localization and classification of skin disease with erythema

Son, Ha Min; Jeon, Wooho; Kim, Jinhyun; Heo, Chan Yeong; Yoon, Hye Jin; Park, Ji Ung; Chung, Tai Myoung

doi:10.1038/s41598-021-84593-z

Cited by 20 publications

(14 citation statements)

References 19 publications

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“…Deep learning-based approaches have recently gained popularity in skin pigment analysis, especially in the segmentation of erythema and hyperpigmented areas [12][13][14]. Regression analysis is a commonly used technique that a neural network architecture learns the relationship between input images and ground truths to predict desired outputs.…”

Section: Introductionmentioning

confidence: 99%

Deep learning‐based pigment analysis model trained with optical approach and ground truth assistance

Jung,

Kim,

Lee

et al. 2023

Journal of Biophotonics

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This study introduces an integrated training method combining the optical approach with ground truth for skin pigment analysis. Deep learning is increasingly applied to skin pigment analysis, primarily melanin and hemoglobin. While regression analysis is a widely used training method to predict ground truth‐like outputs, the input image resolution is restricted by computational resources. The optical approach‐based regression method can alleviate this problem, but compromises performance. We propose a strategy to overcome the limitation of image resolution while preserving performance by incorporating ground truth within the optical approach‐based learning structure. The proposed model decomposes skin images into melanin, hemoglobin, and shading maps, reconstructing them by solving the forward problem with reference to the ground truth for pigments. Evaluation against the VISIA system, a professional diagnostic equipment, yields correlation coefficients of 0.978 for melanin and 0.975 for hemoglobin. Furthermore, our model can produce pigment‐modified images for applications like simulating treatment effects.This article is protected by copyright. All rights reserved.

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Section: Introductionmentioning

confidence: 99%

Deep learning‐based pigment analysis model trained with optical approach and ground truth assistance

Jung,

Kim,

Lee

et al. 2023

Journal of Biophotonics

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“…Furthermore, unlike existing techniques based on expert judgement grading [4], the developed system can provide not only quantified values, such as the number of diagnostic points, average size, total area, and intensity of the entire facial skin, but also visual information, such as the appearance of the location. Providing such diagnostic results requires a lot of time and effort in preparing image training data; however, it is very useful for researchers because it can provide more quantified and visualized information [1,2]. These advantages were also demonstrated in another study of changes in facial skin characteristics with age in Korean women.…”

Section: Discussionmentioning

confidence: 99%

“…The use of skin images for diagnostic purposes has been widespread in the fields of medicine and skin research. However, recent advances in artificial intelligence (AI) learning technologies, particularly deep learning, have accelerated the development of diagnostic tools using skin images [1,2]. Previous diagnostic methods relied on simple feature extraction methods, which made it difficult to account for complex changes in skin characteristics.…”

Section: Introductionmentioning

confidence: 99%

Development and application of artificial intelligence‐based facial skin image diagnosis system: Changes in facial skin characteristics with ageing in Korean women

Park,

Lee

et al. 2023

Intern J of Cosmetic Sci

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ObjectiveTo develop and validate an artificial intelligence (AI) based diagnostic system for analyzing facial skin images using expert judgments and explore its feasibility for skin aging research, specifically by evaluating facial skin changes in Korean women of various ages.MethodsOur AI‐based facial skin diagnosis system (█████████) uses facial images of Korean women to analyze wrinkles, pigmentation, skin pores, and other skin red spots. The system is trained using clinical expert evaluations and deep learning. We assessed the system's precision and sensitivity by analyzing the correlation between the diagnoses by the AI system and those of the experts. We used 120 images of Korean women aged 10–60 years to evaluate the changes in various facial skin characteristics with aging.ResultsThe precision and sensitivity of the developed system were excellent (>.9%), and the diagnosis scores using the detected area and intensity of each item were correlated significantly higher with the visual evaluation results of the clinical experts (>.8, p<.001). We also analyzed facial images of Korean women aged 10–60 years to quantify changes in the scores of wrinkles, pigmentation, and skin pores with age. We identified the age group with the most significant changes as 20 to 30s. Analysis of the detailed skin characteristics of each item showed that wrinkles and pigmentation changed significantly in the 20‐30s, and skin pores increased significantly in the 10‐20s. There was no significant correlation with age or change according to the age group for skin red spots.ConclusionDeveloped AI‐based facial skin diagnosis system can automatically diagnose skin conditions based on clinical expert judgment using only photographic images and analyze various items in detail, quantitatively, and visually. This AI system can provide new and useful approaches in research areas that require a lot of resources and different characterizations, such as the study of facial skin aging.

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“…It also helps in reaching a representation. That is a powerful feature because it helps identify the objects in the image regardless of where they are [29], [30]. Adding fully connected layer is an inexpensive way to teach non-linear components of these properties.…”

Section: Algorithmmentioning

confidence: 99%

A proposed architecture for convolutional neural networks to detect skin cancers

Ahmed

Kashmola

2022

IJ-AI

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The goal of the research paper is to design and development of a computer-based system for the segmentation and classification of malignant skin diseases and a comparison between the accuracy of their detection, as two malignant diseases of skin diseases were detected. Namely, basal cell carcinoma and melanoma separately with images of nevus, and the images were collected from the ISIC 2020 archive group, as the total, The images used: 17,846 images include 3,008 images of basal cell carcinoma (BCC), 5,272 images of melanoma, and 9,566 images of a nevus, and validation data contains 20% of the images used which are not classified and randomly taken from the set of images, and the final test data contains 1,500 anonymous images. An architecture for the convolutional neural network technology in deep learning has been proposed that consists of a set of layers for processing. Processing raw input images for a group of pre-treatment transformations, the data augmentation process, so the number of images used became 86094 images of nevus, 27,072 images of BCC, and 47,448 images of melanoma. Through the detection process, the classification and detection accuracy of BCC was 98.25%, which is higher than the classification accuracy of melanoma is 91.61%.

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AI-based localization and classification of skin disease with erythema

Cited by 20 publications

References 19 publications

Deep learning‐based pigment analysis model trained with optical approach and ground truth assistance

Deep learning‐based pigment analysis model trained with optical approach and ground truth assistance

Development and application of artificial intelligence‐based facial skin image diagnosis system: Changes in facial skin characteristics with ageing in Korean women

A proposed architecture for convolutional neural networks to detect skin cancers

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