Deep-Learning-Based Morphological Feature Segmentation for Facial Skin Image Analysis

Yoon, Huisu; Kim, S. Gene; Lee, Jong-Ha; Yoo, Sangwook

doi:10.3390/diagnostics13111894

Cited by 10 publications

(2 citation statements)

References 27 publications

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“…S. Arshad [17] and colleagues proposed Dermo-Seg, which used ResNet-50 and a mixed loss function to effectively detect the differential structures of lesions. H. Yoon [18] and colleagues proposed a deep-learning-based method for the simultaneous segmentation of wrinkles and pores. Unlike color-based skin analysis, this method relies on the analysis of the morphological structure of the skin.…”

Section: Introductionmentioning

confidence: 99%

DTONet a Lightweight Model for Melanoma Segmentation

Hao,

Wang,

Chen

et al. 2024

Bioengineering

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With the further development of neural networks, automatic segmentation techniques for melanoma are becoming increasingly mature, especially under the conditions of abundant hardware resources. This allows for the accuracy of segmentation to be improved by increasing the complexity and computational capacity of the model. However, a new problem arises when it comes to actual applications, as there may not be the high-end hardware available, especially in hospitals and among the general public, who may have limited computing resources. In response to this situation, this paper proposes a lightweight deep learning network that can achieve high segmentation accuracy with minimal resource consumption. We introduce a network called DTONet (double-tailed octave network), which was specifically designed for this purpose. Its computational parameter count is only 30,859, which is 1/256th of the mainstream UNet model. Despite its reduced complexity, DTONet demonstrates superior performance in terms of accuracy, with an IOU improvement over other similar models. To validate the generalization capability of this model, we conducted tests on the PH2 dataset, and the results still outperformed existing models. Therefore, the proposed DTONet network exhibits excellent generalization ability and is sufficiently outstanding.

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Section: Introductionmentioning

confidence: 99%

DTONet a Lightweight Model for Melanoma Segmentation

Hao,

Wang,

Chen

et al. 2024

Bioengineering

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“…Facial skin disorders commonly occur among people, which is why various studies are being conducted to detect them [1][2][3][4]. Out of these, acne is a prevalent skin disorder that frequently occurs in the general population.…”

Section: Introductionmentioning

confidence: 99%

Semi-Supervised Facial Acne Segmentation Using Bidirectional Copy–Paste

Kim,

Yoon,

Lee

2024

Diagnostics

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Facial acne is a prevalent dermatological condition regularly observed in the general population. However, it is important to detect acne early as the condition can worsen if not treated. For this purpose, deep-learning-based methods have been proposed to automate detection, but acquiring acne training data is not easy. Therefore, this study proposes a novel deep learning model for facial acne segmentation utilizing a semi-supervised learning method known as bidirectional copy–paste, which synthesizes images by interchanging foreground and background parts between labeled and unlabeled images during the training phase. To overcome the lower performance observed in the labeled image training part compared to the previous methods, a new framework was devised to directly compute the training loss based on labeled images. The effectiveness of the proposed method was evaluated against previous semi-supervised learning methods using images cropped from facial images at acne sites. The proposed method achieved a Dice score of 0.5205 in experiments utilizing only 3% of labels, marking an improvement of 0.0151 to 0.0473 in Dice score over previous methods. The proposed semi-supervised learning approach for facial acne segmentation demonstrated an improvement in performance, offering a novel direction for future acne analysis.

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