Skin melanoma segmentation using recurrent and convolutional neural networks

Attia, Mohamed; Hossny, Mohammed; Nahavandi, Saeid; Yazdabadi, Anousha

doi:10.1109/isbi.2017.7950522

Cited by 79 publications

(30 citation statements)

References 11 publications

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“…ABCD rule traits) [54]. The earlier attempts (to the best of our knowledge) in applying deep learning to melanoma detection were proposed in 2015 in [99], [100] (in Japanese) and [101]. Fig.…”

Section: Discussionmentioning

confidence: 99%

Automating the ABCD Rule for Melanoma Detection: A Survey

Ali

Yang

2020

IEEE Access

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The ABCD rule is a simple framework that physicians, novice dermatologists and nonphysicians can use to learn about the features of melanoma in its early curable stage, enhancing thereby the early detection of melanoma. Since the interpretation of the ABCD rule traits is subjective, different solutions have been proposed in literature to tackle such subjectivity and provide objective evaluations to the different traits. This paper reviews the main contributions in literature towards automating asymmetry, border irregularity, color variegation and diameter, where the different methods involved have been highlighted. This survey could serve as an essential reference for researchers interested in automating the ABCD rule.INDEX TERMS Image processing, machine learning, melanoma detection.GUANG YANG received the M.Sc. degree in vision imaging and virtual environments from the Department of Computer Science, University College London, in 2006, and the Ph.D. degree in medical image analysis jointly from the CMIC,

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Section: Discussionmentioning

confidence: 99%

Automating the ABCD Rule for Melanoma Detection: A Survey

Ali

Yang

2020

IEEE Access

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“…Training images do not even require often preprocessing. For this purpose, they were used a hybrid deep learning approach based on CNN and recurrent neural network (RNN) [5]. Often researchers use use deep learning [9,36] and a Deep Residual Network (DRN or ResNet).…”

Section: Segmentation and Classification Methodsmentioning

confidence: 99%

Selected Applications of Deep Neural Networks in Skin Lesion Diagnostic

Michalska

2021

IAPGOS

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The article provides an overview of selected applications of deep neural networks in the diagnosis of skin lesions from human dermatoscopic images, including many dermatological diseases, including very dangerous malignant melanoma. The lesion segmentation process, features selection and classification was described. Application examples of binary and multiclass classification are given. The described algorithms have been widely used in the diagnosis of skin lesions. The effectiveness, specificity, and accuracy of classifiers were compared and analysed based on available datasets.

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“…A hybrid approach that uses convolutional and recurrent neural networks was proposed by Attia et al [11]. The approach was tested on the ISIC 2016 challenge (International Skin Imaging Collaboration) including 900 training images and 375 test images, without making any pre-processing.…”

Section: Related Workmentioning

confidence: 99%

Supervised Versus Unsupervised Deep Learning Based Methods for Skin Lesion Segmentation in Dermoscopy Images

Ali

Trappenberg

2019

Advances in Artificial Intelligence

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Image segmentation is considered a crucial step in automatic dermoscopic image analysis as it affects the accuracy of subsequent steps. The huge progress in deep learning has recently revolutionized the image recognition and computer vision domains. In this paper, we compare a supervised deep learning based approach with an unsupervised deep learning based approach for the task of skin lesion segmentation in dermoscopy images. Results show that, by using the default parameter settings and network configurations proposed in the original approaches, although the unsupervised approach could detect fine structures of skin lesions in some occasions, the supervised approach shows much higher accuracy in terms of Dice coefficient and Jaccard index compared to the unsupervised approach, resulting in 77.7% vs. 40% and 67.2% vs. 30.4%, respectively. With a proposed modification to the unsupervised approach, the Dice and Jaccard values improved to 54.3% and 44%, respectively.

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Skin melanoma segmentation using recurrent and convolutional neural networks

Cited by 79 publications

References 11 publications

Automating the ABCD Rule for Melanoma Detection: A Survey

Automating the ABCD Rule for Melanoma Detection: A Survey

Selected Applications of Deep Neural Networks in Skin Lesion Diagnostic

Supervised Versus Unsupervised Deep Learning Based Methods for Skin Lesion Segmentation in Dermoscopy Images

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