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

Liu, Lin; Chen, Liang; Xue, Yuzhou; Chen, Tingqiao; Chen, Yangmei; Lan, Yufan; Wen, Jia-mei; Shao, Xinyi; Chen, J.

doi:10.1007/s13555-022-00874-z

Cited by 12 publications

(6 citation statements)

References 36 publications

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“…The Swin transformer is a new model of computer vision proposed in 2021 with a wide applicability for tasks including image segmentation, image recovery, and image reconstruction [144,145]. Currently, only a limited number of studies have reported the use of this model for medical images [146][147][148]. The Swin transformer, through its hierarchical structure and selfattentive mechanism, has shown powerful feature extraction and modeling capabilities in medical image processing, providing potential opportunities for improvement and innovation in medical image analysis and diagnosis [149].…”

Section: Actively Explore Innovative Models and Methodsmentioning

confidence: 99%

Recent Advancements and Perspectives in the Diagnosis of Skin Diseases Using Machine Learning and Deep Learning: A Review

Zhang,

Zhong,

et al. 2023

Diagnostics

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Objective: Skin diseases constitute a widespread health concern, and the application of machine learning and deep learning algorithms has been instrumental in improving diagnostic accuracy and treatment effectiveness. This paper aims to provide a comprehensive review of the existing research on the utilization of machine learning and deep learning in the field of skin disease diagnosis, with a particular focus on recent widely used methods of deep learning. The present challenges and constraints were also analyzed and possible solutions were proposed. Methods: We collected comprehensive works from the literature, sourced from distinguished databases including IEEE, Springer, Web of Science, and PubMed, with a particular emphasis on the most recent 5-year advancements. From the extensive corpus of available research, twenty-nine articles relevant to the segmentation of dermatological images and forty-five articles about the classification of dermatological images were incorporated into this review. These articles were systematically categorized into two classes based on the computational algorithms utilized: traditional machine learning algorithms and deep learning algorithms. An in-depth comparative analysis was carried out, based on the employed methodologies and their corresponding outcomes. Conclusions: Present outcomes of research highlight the enhanced effectiveness of deep learning methods over traditional machine learning techniques in the field of dermatological diagnosis. Nevertheless, there remains significant scope for improvement, especially in improving the accuracy of algorithms. The challenges associated with the availability of diverse datasets, the generalizability of segmentation and classification models, and the interpretability of models also continue to be pressing issues. Moreover, the focus of future research should be appropriately shifted. A significant amount of existing research is primarily focused on melanoma, and consequently there is a need to broaden the field of pigmented dermatology research in the future. These insights not only emphasize the potential of deep learning in dermatological diagnosis but also highlight directions that should be focused on.

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Section: Actively Explore Innovative Models and Methodsmentioning

confidence: 99%

Recent Advancements and Perspectives in the Diagnosis of Skin Diseases Using Machine Learning and Deep Learning: A Review

Zhang,

Zhong,

et al. 2023

Diagnostics

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“…This presents promising opportunities for innovation in medical image analysis and diagnosis. Currently, there have been limited studies reporting the utilization of Swin Transformer in the context of medical images 51 – 53 . As far as we are aware, our study stands out as one of the relatively few instances where the Swin Transformer has been applied to the analysis of dermoscopic images.…”

Section: Discussionmentioning

confidence: 99%

Optimizing vitiligo diagnosis with ResNet and Swin transformer deep learning models: a study on performance and interpretability

Zhong,

He,

et al. 2024

Sci Rep

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Vitiligo is a hypopigmented skin disease characterized by the loss of melanin. The progressive nature and widespread incidence of vitiligo necessitate timely and accurate detection. Usually, a single diagnostic test often falls short of providing definitive confirmation of the condition, necessitating the assessment by dermatologists who specialize in vitiligo. However, the current scarcity of such specialized medical professionals presents a significant challenge. To mitigate this issue and enhance diagnostic accuracy, it is essential to build deep learning models that can support and expedite the detection process. This study endeavors to establish a deep learning framework to enhance the diagnostic accuracy of vitiligo. To this end, a comparative analysis of five models including ResNet (ResNet34, ResNet50, and ResNet101 models) and Swin Transformer series (Swin Transformer Base, and Swin Transformer Large models), were conducted under the uniform condition to identify the model with superior classification capabilities. Moreover, the study sought to augment the interpretability of these models by selecting one that not only provides accurate diagnostic outcomes but also offers visual cues highlighting the regions pertinent to vitiligo. The empirical findings reveal that the Swin Transformer Large model achieved the best performance in classification, whose AUC, accuracy, sensitivity, and specificity are 0.94, 93.82%, 94.02%, and 93.5%, respectively. In terms of interpretability, the highlighted regions in the class activation map correspond to the lesion regions of the vitiligo images, which shows that it effectively indicates the specific category regions associated with the decision-making of dermatological diagnosis. Additionally, the visualization of feature maps generated in the middle layer of the deep learning model provides insights into the internal mechanisms of the model, which is valuable for improving the interpretability of the model, tuning performance, and enhancing clinical applicability. The outcomes of this study underscore the significant potential of deep learning models to revolutionize medical diagnosis by improving diagnostic accuracy and operational efficiency. The research highlights the necessity for ongoing exploration in this domain to fully leverage the capabilities of deep learning technologies in medical diagnostics.

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“…(2) cosmetics usage and makeup tutorial [14,41,68,84], (3) skin physical assessment [13,33,88], (4) skin condition diagnosis [27,45,82,87], (5) treatment & skincare products recommendation [1,32,55,66], (6) treatment outcome prediction [6,53,63], etc. The above work mainly involves skin imaging technology, which makes contributions to skin diagnosis and treatment by using artificial intelligence to understand various types of image information.…”

Section: Computer-aided Skincare Tools In Hci Communitymentioning

confidence: 99%

“It is hard to remove from my eye”: Design Makeup Residue Visualization System for Chinese Traditional Opera (Xiqu) Performers

Xiong,

Fu,

Zhu

et al. 2024

Proceedings of the CHI Conference on Human Factors in Computing Systems

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Chinese traditional opera (Xiqu) performers often experience skin problems due to the long-term use of heavy-metal-laden face paints. To explore the current skincare challenges encountered by Xiqu performers, we conducted an online survey (N=136) and semistructured interviews (N=15) as a formative study. We found that incomplete makeup removal is the leading cause of human-induced skin problems, especially the difficulty in removing eye makeup. Therefore, we proposed EyeVis, a prototype that can visualize the residual eye makeup and record the time make-up was worn by Xiqu performers. We conducted a 7-day deployment study (N=12) to evaluate EyeVis. Results indicate that EyeVis helps to increase Xiqu performers' awareness about removing makeup, as well as boosting their confidence and security in skincare. Overall, this work also provides implications for studying the work of people who wear makeup on a daily basis, and helps to promote and preserve the intangible cultural heritage of practitioners.CCS Concepts: • Human-centered computing → Empirical studies in HCI; Assistive technologies.

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

Cited by 12 publications

References 36 publications

Recent Advancements and Perspectives in the Diagnosis of Skin Diseases Using Machine Learning and Deep Learning: A Review

Recent Advancements and Perspectives in the Diagnosis of Skin Diseases Using Machine Learning and Deep Learning: A Review

Optimizing vitiligo diagnosis with ResNet and Swin transformer deep learning models: a study on performance and interpretability

“It is hard to remove from my eye”: Design Makeup Residue Visualization System for Chinese Traditional Opera (Xiqu) Performers

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