KIEGLFN: A unified acne grading framework on face images

Yi, Lin; Jiang, Junqiu; Ma, Zhaoyang; Chen, Dongxin; Guan, Yi; You, Haiyan; Cheng, Xue; Liu, Bingmei; Luo, Gongning

doi:10.1016/j.cmpb.2022.106911

Cited by 16 publications

(11 citation statements)

References 35 publications

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“…For proposing a model, the use of GANs in acne detection is intriguing due to their potential point of view for disentanglement of detection and related feature extraction with attributes of severity analysis. The concept of deidentification in GAN-enabling processes is critical; it helps in the recognition of facial features and attributes of their patterns with the original one while executing image-to-image analysis [10]. In fact, it preserves the changes that appear in the face image during analysis according to the feature of interest, especially skin lesions.…”

Section: Figure 1 the Current Process Of Acne Detectionmentioning

confidence: 99%

“…On the other side, there are a few challenging prospects that effects the employment of DNN with other state-of-the-art methods for the better result of acne severity. Generative Adversarial Network (GAN) is associated with generative modeling, which is an unsupervised learning approach based on automation to discover learning and patterns of input values in accordance with the regulations [8,9,10]. In such a way that the model can be used to give output as a new example drawn from the original one.…”

Section: Figure 1 the Current Process Of Acne Detectionmentioning

confidence: 99%

“…Thus, with the feature of high-resolution data utility, the technology holds the feature of privacy protection by examining the cluster of complete changes in terms of facial acne detection in the image with automation. In addition, recent publications in the detection of acne have focused mainly on the severity of diagnoses using single images instead of associating information of patients for efficient and effective personalized diagnosis [10]- [14]. In this manner, an automatic assessment of acne detection is required, where personalization may put a great impact throughout the examination that acquires without regard to time and location.…”

Section: Figure 1 the Current Process Of Acne Detectionmentioning

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: Figure 1 the Current Process Of Acne Detectionmentioning

confidence: 99%

Section: Figure 1 the Current Process Of Acne Detectionmentioning

confidence: 99%

Section: Figure 1 the Current Process Of Acne Detectionmentioning

confidence: 99%

See 1 more Smart Citation

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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“…Jerawat (acne vulgaris) merupakan salah satu penyakit dermatologi paling umum pada kehidupan sehari-hari yang menyerang orang-orang dari segala usia terutama di masa remaja [1]. Lebih dari 80% orang mengalami permasalahan jerawat pada masa remajanya [2].…”

Section: Pendahuluanunclassified

Deteksi dan Visualisasi Berbasis Computer Vision untuk Analisis Gambar Dermatologis dalam Penilaian Keparahan Jerawat

Watef,

Mahananto

2024

ilkomnika

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Jerawat (acne vulgaris) merupakan salah satu penyakit dermatologis paling umum dalam kehidupan sehari-hari utamanya pada masa remaja. Lebih dari 80% orang mengalami permasalahan jerawat pada masa remajanya. Dalam menilai tingkat keparahan jerawat perlu dilakukan penilaian secara obyektif dengan prosedur perawatan medis. Penilaian jerawat secara tradisional umumnya dilakukan oleh dokter kulit dengan menentukan jenis dan jumlah lesi dimana sering kali penilaian ini memakan waktu dan mengganggu efisiensi dalam penilaian jerawat. Kerangka kerja ini menguji praproses gambar dengan menggunakan metode red color detection, threshold, enhanced image dan penggabungan enhanced image dan red color detection dalam klasifikasi gambar jerawat. Selain itu beberapa metode klasifikasi diterapkan untuk menguji data masukan dari penerapan praproses yang dilakukan. Adapun metode klasifikasi yang digunakan adalah NN, SVM, KNN dan VGG16 selain itu penerapan fitur ekstraksi juga diterapkan. Penggunaan fitur ekstraksi menggunakan VGG16 dilanjutkan dengan Principal Component Analysis (PCA) dimana penerapan PCA dilakukan untuk meningkatkan efisiensi komputasional. Dengan data masukan enhanced image yang kemudian diklasifikasikan menggunakan VGG16 memberikan hasil akurasi terbaik mencapai 69% . Kedepannya metode praproses enhanced image dapat diterapkan pada kumpulan data yang berbeda dikarenakan tidak dipungkiri bahwasanya kumpulan data ACNE04 memiliki banyak noise pada citra.

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“…Yang et al provides acne assessment and treatment recommendations based on current acne management guidelines in China, but do not classify and quantify specific skin lesions ( 20 ). Lin's team proposed a global assessment grading framework simulating dermatologist diagnosis to adapt to various grading criteria ( 21 ). In 2022, a study used the Global Assessment (IGA) scale to grade the severity of acne, with counts of various acne types as inputs, and the model was 6.8% more accurate than physician diagnoses ( 22 ).…”

Section: Introductionmentioning

confidence: 99%

Advancements in acne detection: application of the CenterNet network in smart dermatology

Zhang,

Li,

Shi

et al. 2024

Front. Med.

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IntroductionAcne detection is critical in dermatology, focusing on quality control of acne imagery, precise segmentation, and grading. Traditional research has been limited, typically concentrating on singular aspects of acne detection.MethodsWe propose a multi-task acne detection method, employing a CenterNet-based training paradigm to develop an advanced detection system. This system collects acne images via smartphones and features multi-task capabilities for detecting image quality and identifying various acne types. It differentiates between noninflammatory acne, papules, pustules, nodules, and provides detailed delineation for cysts and post-acne scars.ResultsThe implementation of this multi-task learning-based framework in clinical diagnostics demonstrated an 83% accuracy in lesion categorization, surpassing ResNet18 models by 12%. Furthermore, it achieved a 76% precision in lesion stratification, outperforming dermatologists by 16%.DiscussionOur framework represents a advancement in acne detection, offering a comprehensive tool for classification, localization, counting, and precise segmentation. It not only enhances the accuracy of remote acne lesion identification by doctors but also clarifies grading logic and criteria, facilitating easier grading judgments.

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KIEGLFN: A unified acne grading framework on face images

Cited by 16 publications

References 35 publications

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

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

Deteksi dan Visualisasi Berbasis Computer Vision untuk Analisis Gambar Dermatologis dalam Penilaian Keparahan Jerawat

Advancements in acne detection: application of the CenterNet network in smart dermatology

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