Attention-Guided Network with Densely Connected Convolution for Skin Lesion Segmentation

Tao, Shengxin; Jiang, Yun; Cao, Simin; Wu, Chao; Ma, Zeqi

doi:10.3390/s21103462

Cited by 15 publications

(6 citation statements)

References 55 publications

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“…The most common pooling operations are max-pooling and average-pooling: Max-Pooling: The maximum value within the pooling region is selected in this operation. Max-pooling effectively captures the most dominant features within the region, as shown in Eq 5 [ 39 ]. Average-Pooling: Here, the average value of the pooling region is computed.…”

Section: Methodsmentioning

confidence: 99%

Analysis of dermoscopy images of multi-class for early detection of skin lesions by hybrid systems based on integrating features of CNN models

Alshahrani,

Al-Jabbar,

Senan

et al. 2024

PLoS ONE

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Skin cancer is one of the most fatal skin lesions, capable of leading to fatality if not detected in its early stages. The characteristics of skin lesions are similar in many of the early stages of skin lesions. The AI in categorizing diverse types of skin lesions significantly contributes to and helps dermatologists to preserve patients’ lives. This study introduces a novel approach that capitalizes on the strengths of hybrid systems of Convolutional Neural Network (CNN) models to extract intricate features from dermoscopy images with Random Forest (Rf) and Feed Forward Neural Networks (FFNN) networks, leading to the development of hybrid systems that have superior capabilities early detection of all types of skin lesions. By integrating multiple CNN features, the proposed methods aim to improve the robustness and discriminatory capabilities of the AI system. The dermoscopy images were optimized for the ISIC2019 dataset. Then, the area of the lesions was segmented and isolated from the rest of the image by a Gradient Vector Flow (GVF) algorithm. The first strategy for dermoscopy image analysis for early diagnosis of skin lesions is by the CNN-RF and CNN-FFNN hybrid models. CNN models (DenseNet121, MobileNet, and VGG19) receive a region of interest (skin lesions) and produce highly representative feature maps for each lesion. The second strategy to analyze the area of skin lesions and diagnose their type by means of CNN-RF and CNN-FFNN hybrid models based on the features of the combined CNN models. Hybrid models based on combined CNN features have achieved promising results for diagnosing dermoscopy images of the ISIC 2019 dataset and distinguishing skin cancers from other skin lesions. The Dense-Net121-MobileNet-RF hybrid model achieved an AUC of 95.7%, an accuracy of 97.7%, a precision of 93.65%, a sensitivity of 91.93%, and a specificity of 99.49%.

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Section: Methodsmentioning

confidence: 99%

Analysis of dermoscopy images of multi-class for early detection of skin lesions by hybrid systems based on integrating features of CNN models

Alshahrani,

Al-Jabbar,

Senan

et al. 2024

PLoS ONE

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show abstract

“…Most of the traditional medical image segmentation methods are based on boundary detection [ 23 ], threshold-based segmentation [ 24 ] and machine learning-based algorithms. Although these methods achieved notable segmentation performance, they rely excessively on manual feature selection and the introduction of a priori information [ 25 ]. Benefiting from the rapid development of deep learning, CNN-based segmentation methods have dominated the field of medical image segmentation.…”

Section: Related Workmentioning

confidence: 99%

iU-Net: a hybrid structured network with a novel feature fusion approach for medical image segmentation

et al. 2023

Self Cite

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In recent years, convolutional neural networks (CNNs) have made great achievements in the field of medical image segmentation, especially full convolutional neural networks based on U-shaped structures and skip connections. However, limited by the inherent limitations of convolution, CNNs-based methods usually exhibit limitations in modeling long-range dependencies and are unable to extract large amounts of global contextual information, which deprives neural networks of the ability to adapt to different visual modalities. In this paper, we propose our own model, which is called iU-Net bacause its structure closely resembles the combination of i and U. iU-Net is a multiple encoder-decoder structure combining Swin Transformer and CNN. We use a hierarchical Swin Transformer structure with shifted windows as the primary encoder and convolution as the secondary encoder to complement the context information extracted by the primary encoder. To sufficiently fuse the feature information extracted from multiple encoders, we design a feature fusion module (W-FFM) based on wave function representation. Besides, a three branch up sampling method(Tri-Upsample) has developed to replace the patch expand in the Swin Transformer, which can effectively avoid the Checkerboard Artifacts caused by the patch expand. On the skin lesion region segmentation task, the segmentation performance of iU-Net is optimal, with Dice and Iou reaching 90.12% and 83.06%, respectively. To verify the generalization of iU-Net, we used the model trained on ISIC2018 dataset to test on PH2 dataset, and achieved 93.80% Dice and 88.74% IoU. On the lung feild segmentation task, the iU-Net achieved optimal results on IoU and Precision, reaching 98.54% and 94.35% respectively. Extensive experiments demonstrate the segmentation performance and generalization ability of iU-Net.

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“…Dong et al proposed a feedback attention network based on a context encoder network for skin lesion segmentation [ 13 ]. Tao et al proposed another attention block named channel spatial fast attention-guided filter for the densely connected convolution network [ 14 ]. Wu et al proposed an adaptive dual attention module to refine the extracted features for upsampling operations [ 15 ].…”

Section: Related Workmentioning

confidence: 99%

A Workflow for Computer-Aided Evaluation of Keloid Based on Laser Speckle Contrast Imaging and Deep Learning

Wang

Xiao

et al. 2022

JPM

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A keloid results from abnormal wound healing, which has different blood perfusion and growth states among patients. Active monitoring and treatment of actively growing keloids at the initial stage can effectively inhibit keloid enlargement and has important medical and aesthetic implications. LSCI (laser speckle contrast imaging) has been developed to obtain the blood perfusion of the keloid and shows a high relationship with the severity and prognosis. However, the LSCI-based method requires manual annotation and evaluation of the keloid, which is time consuming. Although many studies have designed deep-learning networks for the detection and classification of skin lesions, there are still challenges to the assessment of keloid growth status, especially based on small samples. This retrospective study included 150 untreated keloid patients, intensity images, and blood perfusion images obtained from LSCI. A newly proposed workflow based on cascaded vision transformer architecture was proposed, reaching a dice coefficient value of 0.895 for keloid segmentation by 2% improvement, an error of 8.6 ± 5.4 perfusion units, and a relative error of 7.8% ± 6.6% for blood calculation, and an accuracy of 0.927 for growth state prediction by 1.4% improvement than baseline.

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Attention-Guided Network with Densely Connected Convolution for Skin Lesion Segmentation

Cited by 15 publications

References 55 publications

Analysis of dermoscopy images of multi-class for early detection of skin lesions by hybrid systems based on integrating features of CNN models

Analysis of dermoscopy images of multi-class for early detection of skin lesions by hybrid systems based on integrating features of CNN models

iU-Net: a hybrid structured network with a novel feature fusion approach for medical image segmentation

A Workflow for Computer-Aided Evaluation of Keloid Based on Laser Speckle Contrast Imaging and Deep Learning

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