Serial attention network for skin lesion segmentation

Ren, Yuan; Yu, Long; Tian, Shengwei; Cheng, Junlong; Guo, Zhiqi; Zhang, Yanhan

doi:10.1007/s12652-021-02933-3

Cited by 21 publications

(17 citation statements)

References 38 publications

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“…Their technique produced promising results for lesion classification. Ren et al [ 23 ] presented a fusion mechanism for the segmentation of a skin lesion. The spatial attention and channel attention modules extracted the information from channels of skin images.…”

Section: Related Workmentioning

confidence: 99%

Multiclass Skin Lesion Classification Using Hybrid Deep Features Selection and Extreme Learning Machine

Afza

Sharif

Khan

et al. 2022

Sensors

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The variation in skin textures and injuries, as well as the detection and classification of skin cancer, is a difficult task. Manually detecting skin lesions from dermoscopy images is a difficult and time-consuming process. Recent advancements in the domains of the internet of things (IoT) and artificial intelligence for medical applications demonstrated improvements in both accuracy and computational time. In this paper, a new method for multiclass skin lesion classification using best deep learning feature fusion and an extreme learning machine is proposed. The proposed method includes five primary steps: image acquisition and contrast enhancement; deep learning feature extraction using transfer learning; best feature selection using hybrid whale optimization and entropy-mutual information (EMI) approach; fusion of selected features using a modified canonical correlation based approach; and, finally, extreme learning machine based classification. The feature selection step improves the system’s computational efficiency and accuracy. The experiment is carried out on two publicly available datasets, HAM10000 and ISIC2018. The achieved accuracy on both datasets is 93.40 and 94.36 percent. When compared to state-of-the-art (SOTA) techniques, the proposed method’s accuracy is improved. Furthermore, the proposed method is computationally efficient.

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Section: Related Workmentioning

confidence: 99%

Multiclass Skin Lesion Classification Using Hybrid Deep Features Selection and Extreme Learning Machine

Afza

Sharif

Khan

et al. 2022

Sensors

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“…Attention mechanism introduced in whole network has channel spatial and LSTM adaptive attention mechanism. First, skin lesion regions vary in color, size, and shape; some light lesions are highly similar to normal skin, and boundaries of deeper lesion regions are complex, to segment model with sufficiently dense feature resolution, Ren et al [ 83 ] used serial attention network (SANet) to segment skin lesion regions and introduced channel first followed by a spatial attention mechanism to aggregate global, local, and interchannel information using interdependencies between channel mappings to improve representation of semantic features and using spatial attention to select global contextual information and contextual relationships to make semantic features more compact and consistent; SANet captures interpixel and interchannel feature dependencies and achieves 0.7692 average Jaccard index in ISIC2017 dataset. Second, it is difficult for image caption generation tasks to correctly extract image global features and perform image regions for each word without ignoring partial words, Deng et al [ 84 ] used DenseNet to extract features required for LSTM generation of sentences and introduced LSTM adaptive attention mechanism to improve correspondence problem of forced text and image regions, which was demonstrated on Flickr30k and COCO datasets showed that flexibility of caption generation was improved, with significant improvements in the BLEU and METEOR evaluation criteria.…”

Section: Development Of Densenetmentioning

confidence: 99%

Dense Convolutional Network and Its Application in Medical Image Analysis

Tao

XinYu

et al. 2022

BioMed Research International

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Dense convolutional network (DenseNet) is a hot topic in deep learning research in recent years, which has good applications in medical image analysis. In this paper, DenseNet is summarized from the following aspects. First, the basic principle of DenseNet is introduced; second, the development of DenseNet is summarized and analyzed from five aspects: broaden DenseNet structure, lightweight DenseNet structure, dense unit, dense connection mode, and attention mechanism; finally, the application research of DenseNet in the field of medical image analysis is summarized from three aspects: pattern recognition, image segmentation, and object detection. The network structures of DenseNet are systematically summarized in this paper, which has certain positive significance for the research and development of DenseNet.

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“…It consists of 10,015 dermoscopic dermatoscopic images which are released as a training set for academic machine learning purposes and are publicly available through the ISIC archive [120]. A dermo- [103] 12,500 D 7 P [24], [29], [48], [58], [59], [104]-[107] ISIC 2017 [108] ∼2,000 D 3 P [46], [85], [104], [109] ISBI 2016 [110] 1,279 D 2 P [28], [76], [77], [90], [101], [111]- [113] ISIC Archive(2018) [114] 23,665 D 7 P [44], [49], [63], [115]- [119] HAM 10000 [120] 10,015 The interactive atlas of dermoscopy [132] (Atlas) dataset has 1,011 dermoscopic images (252 melanoma and 759 nevi cases), with 7-point checklist criteria. There are also 1,011 clinical color images corresponding to dermoscopic images.…”

Section: Framework Year Features Referencesmentioning

confidence: 99%

“…Moreover, transfer learning is more efficient in classifying between similar lesions, making it a first choice [161]. These papers used transfer learning in the literature we surveyed [25], [26], [28], [30], [33]- [39], [41], [42], [46], [52], [58], [61], [62], [64], [66]- [68], [70]- [73], [75], [76], [76], [77], [85]- [87], [92], [102], [112], [113], [122], [124], [126], [127], [129], [141], [151], [152], [158], [159], [162]- [179]. Transfer learning can transfer the parameters of the trained model (pre-training model) to the new model to help the new model training.…”

Section: B Transfer Learningmentioning

confidence: 99%

Recent Advances in Diagnosis of Skin Lesions Using Dermoscopic Images Based on Deep Learning

et al. 2022

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Skin cancer is one of the most threatening cancers, which spreads to the other parts of the body if not caught and treated early. During the last few years, the integration of deep learning into skin cancer has been a milestone in health care, and dermoscopic images are right at the center of this revolution. This review study focuses on the state-of-the-art automatic diagnosis of skin cancer from dermoscopic images based on deep learning. This work thoroughly explores the existing deep learning and its application in diagnosing dermoscopic images. This study aims to present and summarize the latest methodology in melanoma classification and the techniques to improve this. We discuss advancements in deep learningbased solutions to diagnose skin cancer, along with some challenges and future opportunities to strengthen these automatic systems to support dermatologists and enhance their ability to diagnose skin cancer. INDEX TERMSSkin cancer, Dermoscopy images, Deep learning, Classification, Literature review.FIGURE 1: Global heat map showing estimated agestandardized incidence rates, in 2020, of melanoma of the skin in all sexes, all ages. The map shows melanoma incidence in all parts of the world, except Greenland in the Arctic Circle. The regions most affected by skin melanoma globally are Europe, the United States, Canada, and Australia [2].microscopy-based tool to improve non-invasive diagnostic discrimination of skin lesions based on color and structure analysis [5]. This paper focuses on dermoscopy images. Because dermoscopic structures have direct histopathologic correlates, dermoscopic images help the dermatologist select management and treatment options for particular types of skin cancers [6]. In addition, dermoscopy can be useful for

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Serial attention network for skin lesion segmentation

Cited by 21 publications

References 38 publications

Multiclass Skin Lesion Classification Using Hybrid Deep Features Selection and Extreme Learning Machine

Multiclass Skin Lesion Classification Using Hybrid Deep Features Selection and Extreme Learning Machine

Dense Convolutional Network and Its Application in Medical Image Analysis

Recent Advances in Diagnosis of Skin Lesions Using Dermoscopic Images Based on Deep Learning

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