Skin Lesion Segmentation based on Integrating EfficientNet and Residual block into U-Net Neural Network

Nguyen, Duy Khang; Tran, Thi-Thao; Nguyen, Cong Phuong; Pham, Van-Truong

doi:10.1109/gtsd50082.2020.9303084

Cited by 24 publications

(3 citation statements)

References 17 publications

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“…In this section, we quantitatively compare our method with several state‐of‐the‐art lesion segmentation techniques. Specifically, we conduct extensive experiments against prior approaches [7,30–47]. All the TriClick inference adopts the automatic style to mimic the stochasticity imposed by real user interactions in the comparisons.…”

Section: Methodsmentioning

confidence: 99%

TriClick: Interactive Dermoscopic Image Segmentation with Triangle Map

Zhao,

Gu,

et al. 2024

IEEJ Transactions Elec Engng

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Lesion segmentation is a fundamental task that has been widely studied in biomedicine. State‐of‐the‐art methods still struggle with the inherent challenges of dermoscopic images such as uncertain boundaries or severe occlusion. In this study, we argue that this task can be better resolved by introducing user interaction and proposing a deep interactive framework for lesion segmentation. Our method is designed to best reflect user expertise to iteratively refine the segmentation results. In each iteration, professional users are expected to click three points on the boundary of the lesion to encourage the network to update the previous round of the segmentation result. To best exploit the clues from the interaction, we explore a novel encoding strategy by producing a triangle map whose vertices are the three clicked points. Owing to the strong guidance within such triangle maps, our method can outperform state‐of‐the‐art methods with baseline segmentation backbones. Extensive experimental results and ablation studies on three commonly used lesion segmentation data sets demonstrated the effectiveness of our method in terms of segmentation accuracy and encoding effectiveness. © 2024 The Authors. IEEJ Transactions on Electrical and Electronic Engineering published by Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

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Section: Methodsmentioning

confidence: 99%

TriClick: Interactive Dermoscopic Image Segmentation with Triangle Map

Zhao,

Gu,

et al. 2024

IEEJ Transactions Elec Engng

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“…A Feature Pyramid is used in the last layer to reduce memory usage. Nguyen et al [37] modified the U-net architecture by replacing the original encoder with an EfficientNet and the decoder is built with residual blocks from ResNet. This strategy aimed to use ResNet and EfficientNet architectures to avoid overfitting and maintain the efficient reception field size.…”

Section: Skin Lesion Segmentationmentioning

confidence: 99%

Deep Learning Techniques Applied to Skin Lesion Classification: A Review

Silva

Lazzaretti

Monteiro

2022

2022 International Conference on Machine Learning, Control, and Robotics (MLCR)

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Skin cancer is one of the most common cancers in the world. The most dangerous type of skin cancer is melanoma, which can be lethal if not treated early. However, diagnosing skin lesions can be a difficult task. Therefore, deep learning techniques applied to the diagnosis of skin lesions have been explored by researchers, given their effectiveness in extracting features and classifying input data. In this work, we present a review of latest approaches that apply deep learning techniques to skin lesion classification task. In addition, some datasets used for training and validating the models are introduced, informing their characteristics and specificities, as well as popular pre-processing steps and skin lesion segmentation approaches. Finally, we comment the effectiveness of the proposed models.

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“…[24] and FusionNet [25], which are typically utilized for the segmentation of medical pictures, can be adapted for usage in the segmentation of cracks at the pixel level. For crack segmentation, several deep CNN methods using SegNet [26], UNet [27], and their respective variations [28], have been implemented.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Road Crack Localization for Sustainable Road Safety Using HCTNet

Yadav,

Sharma,

Chauhan

et al. 2024

Sustainability

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Road crack detection is crucial for maintaining and inspecting civil infrastructure, as cracks can pose a potential risk for sustainable road safety. Traditional methods for pavement crack detection are labour-intensive and time-consuming. In recent years, computer vision approaches have shown encouraging results in automating crack localization. However, the classical convolutional neural network (CNN)-based approach lacks global attention to the spatial features. To improve the crack localization in the road, we designed a vision transformer (ViT) and convolutional neural networks (CNNs)-based encoder and decoder. In addition, a gated-attention module in the decoder is designed to focus on the upsampling process. Furthermore, we proposed a hybrid loss function using binary cross-entropy and Dice loss to evaluate the model’s effectiveness. Our method achieved a recall, F1-score, and IoU of 98.54%, 98.07%, and 98.72% and 98.27%, 98.69%, and 98.76% on the Crack500 and Crack datasets, respectively. Meanwhile, on the proposed dataset, these figures were 96.89%, 97.20%, and 97.36%.

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Skin Lesion Segmentation based on Integrating EfficientNet and Residual block into U-Net Neural Network

Cited by 24 publications

References 17 publications

TriClick: Interactive Dermoscopic Image Segmentation with Triangle Map

TriClick: Interactive Dermoscopic Image Segmentation with Triangle Map

Deep Learning Techniques Applied to Skin Lesion Classification: A Review

Enhancing Road Crack Localization for Sustainable Road Safety Using HCTNet

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