Automated Facial Acne Lesion Detecting and Counting Algorithm for Acne Severity Evaluation and Its Utility in Assisting Dermatologists

Kim, Dong Hyo; Sun, Sukkyu; Cho, Soo Ick; Kong, Hyoun‐Joong; Lee, Ji Won; Lee, Jun Hyo; Suh, Dae Hun

doi:10.1007/s40257-023-00777-5

Cited by 4 publications

(2 citation statements)

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“…The evolution of Artificial Intelligence (AI) in Information Technology (IT) has created new paradigms in several domains of investigations, such as face detection, face mask recognition, iris detection, and other critical fields of machine analysis [1]. One of the prominent prospects that emerge in AI is to detect face acne and severity qualification.…”

Section: Introductionmentioning

confidence: 99%

“…By analysis of this, it is identified that acne vulgaris is a skin disease that commonly involves chronic inflammation and is additionally characterized as kin with seborrhea, nodules, comedowns, scars, papules, and pimples. As the recent report of the World Health Organization (WHO), which is submitted based on the year 2021-2022, more than 600 million humans, where almost 80% are young adults affected and survive this disease all around the world [1,2,3,4]. During the investigation, it is evaluated that acne disease not only affects the physical but also significantly damages the psychology of the patients.…”

Section: Introductionmentioning

confidence: 99%

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D-GAN: An Automatic Acne Detection, Severity, and Assessment Framework using Generative Adversarial Network with Deep Neural Network

Khalid,

Chen,

Khan

et al. 2024

Preprint

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With the robust development of Artificial Intelligence (AI), especially image processing has made information technology more efficient and effective in the sense to evaluate facial features, even though there has been masked on the face. However, the accuracy of acne detection and related severity analysis is becoming a significant prospect for the precise treatment of patients. Due to this, close severity is one of the features that need to be added first, while it is considered a highly challenging aspect for dermatologists because the similar appearance of acne in the face reduces the rate of accuracy when examining. It poses a serious problem in the domain of biomedical processing and controls. In this paper, we contribute to four different folds. Initially, this paper presents a novel framework that provides a platform in order to measure localization and segmentation. In this process, consultants receive better accuracy and efficiency during the process of acne detection and severity analysis. Second, this paper utilizes deep neural networks (DNNs) as a backend process to lightning the extraction of multi-scale features through a multi-hierarchy neural net for regionalized facial features to investigate distinction and localization. Third, a class-based segmentation approach customizes and integrates with the proposed framework to examine the background and facial skin separation to distinguish different classes to obtain severity marking. Fourth, the facial skin segmentation classes are built as a cluster segment using Generative Adversarial Network (GAN). It provides a high-resolution network that enhances severity masking awareness and related attentions, including shuffle and conditional channels like an update in weight block. Although, the simulation illustrates that the technological collaboration achieves promising results and elaborates on the accuracy and efficiency while the detection of acne as compared to other state-of-the-art baseline methods. In addition, the proposed framework illustrates good results in severity analysis for acne detection comparably far better than the previously published articles with a performance rate of 3.112% (accuracy), 1.131% (segmentation), 2.317% (localization), and 1.573% (GAN-based high-resolution network management), respectively.

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Section: Introductionmentioning

confidence: 99%