Skin lesion analysis toward melanoma detection: A challenge at the 2017 International symposium on biomedical imaging (ISBI), hosted by the international skin imaging collaboration (ISIC)

Codella, Noel; Gutman, David; Celebi, M. Emre; Helba, Brian; Marchetti, Michael A.; Dusza, Stephen W.; Kalloo, Aadi; Liopyris, Konstantinos; Mishra, Nabin K.; Kittler, Harald; Halpern, Allan C.

doi:10.1109/isbi.2018.8363547

Cited by 1,550 publications

(1,032 citation statements)

References 26 publications

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“…In this study, we show that the new algorithm for risk assessment has a high accuracy for self‐assessment of skin lesions for skin cancer. This result is comparable to some of the recently published, best skin cancer disease classification algorithms …”

Section: Discussionsupporting

confidence: 86%

“…Compared to these studies, it is more challenging to obtain high accuracy in our setup, as images taken with a smartphone have a lot more variability than clinical or dermoscopic images. Image acquisition within this SA is partially controlled, since the camera module only accepts a picture if it satisfies some minimal quality requirements, namely the lesion should be present and contained in the image, there should be no shadows or hair covering the lesion, and the picture should be focused and centred.…”

Section: Discussionmentioning

confidence: 99%

“…Access to medical care is being transformed with the advent of mobile applications which can monitor, diagnose and provide health information. In the field of dermatology, there is considerable interest in developing algorithms for image analysis of skin lesions to evaluate the risk of malignant melanoma (MM) and keratinocyte carcinomas (KC) . Early detection of skin cancer is important since the prognosis is highly dependent on whether the cancer is detected at an early stage, when it is easier to treat and its prognosis is more favourable…”

Section: Introductionmentioning

confidence: 99%

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Accuracy of a smartphone application for triage of skin lesions based on machine learning algorithms

Udrea

Mitra

Costea

et al. 2019

Acad Dermatol Venereol

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Background Machine learning algorithms achieve expert‐level accuracy in skin lesion classification based on clinical images. However, it is not yet shown whether these algorithms could have high accuracy when embedded in a smartphone app, where image quality is lower and there is high variability in image taking scenarios by users. In the past, these applications were criticized due to lack of accuracy. Objective In this study, we evaluate the accuracy of the newest version of a smartphone application (SA) for risk assessment of skin lesions. Methods This SA uses a machine learning algorithm to compute a risk rating. The algorithm is trained on 131 873 images taken by 31 449 users in multiple countries between January 2016 and August 2018 and rated for risk by dermatologists. To evaluate the sensitivity of the algorithm, we use 285 histopathologically validated skin cancer cases (including 138 malignant melanomas), from two previously published clinical studies (195 cases) and from the SA user database (90 cases). We calculate the specificity on a separate set from the SA user database containing 6000 clinically validated benign cases. Results The algorithm scored a 95.1% (95% CI, 91.9–97.3%) sensitivity in detecting (pre)malignant conditions (93% for malignant melanoma and 97% for keratinocyte carcinomas and precursors). This level of sensitivity was achieved with a 78.3% (95% CI, 77.2–79.3%) specificity. Conclusions This SA provides a high sensitivity to detect skin cancer; however, there is still room for improvement in terms of specificity. Future studies are needed to assess the impact of this SA on the health systems and its users.

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Section: Discussionsupporting

confidence: 86%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Accuracy of a smartphone application for triage of skin lesions based on machine learning algorithms

Udrea

Mitra

Costea

et al. 2019

Acad Dermatol Venereol

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“…Moreover, in comparison with previously conducted studies on the same ISIC dermoscopic image data set, the proposed method can be considered an effective method. In addition, the studies in References [37,38] can be improved and compared with the proposed method on the same dataset.…”

Section: Comparison With Other Segmentation Methods Using the Isic Armentioning

confidence: 99%

A Novel Skin Lesion Detection Approach Using Neutrosophic Clustering and Adaptive Region Growing in Dermoscopy Images

2018

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This paper proposes novel skin lesion detection based on neutrosophic clustering and adaptive region growing algorithms applied to dermoscopic images, called NCARG. First, the dermoscopic images are mapped into a neutrosophic set domain using the shearlet transform results for the images. The images are described via three memberships: true, indeterminate, and false memberships. An indeterminate filter is then defined in the neutrosophic set for reducing the indeterminacy of the images. A neutrosophic c-means clustering algorithm is applied to segment the dermoscopic images. With the clustering results, skin lesions are identified precisely using an adaptive region growing method. To evaluate the performance of this algorithm, a public data set (ISIC 2017) is employed to train and test the proposed method. Fifty images are randomly selected for training and 500 images for testing. Several metrics are measured for quantitatively evaluating the performance of NCARG. The results establish that the proposed approach has the ability to detect a lesion with high accuracy, 95.3% average value, compared to the obtained average accuracy, 80.6%, found when employing the neutrosophic similarity score and level set (NSSLS) segmentation approach.

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“…The Data Set from the "ISIC 2017: Skin Lesion Analysis Towards Melanoma Detection" challenge dataset [15]. It was collected from the below website (International Skin Imaging Collaboration: Melanoma Project): https://isicarchive.com/#images.…”

Section: B Datasetmentioning

confidence: 99%

Enhanced K-mean Using Evolutionary Algorithms for Melanoma Detection and Segmentation in Skin Images

Aljawawdeh¹,

Imraiziq²,

Aljawawdeh³

2017

ijacsa

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Abstract-Nowadays, Melanoma has become one of the most significant public health concerns. Malignant Melanoma (MM) is considered the most rapidly spreading type of skin cancer. In this paper, we have built models for detection, segmentation, and classification of Melanoma in skin images using evolutionary algorithms. The first step was to enhance the K-mean algorithm by using two kinds of Evolutionary Algorithms: a Genetic Algorithm and the Particle Swarm Algorithm. Then the Enhanced Algorithms and the default k-mean separately were used to do detection and segmentation of skin cancer images. Then a feature extraction step was applied on the segmented images. Finally, the classification step was done by using two predictive models. The first model was built using a Neural Network backpropagation and the other one using some threshold values for some selected features. The results showed a high accuracy using Neural Back-propagation for the Enhanced K-mean by using a Genetic Algorithm, which achieved 87.5%.

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Skin lesion analysis toward melanoma detection: A challenge at the 2017 International symposium on biomedical imaging (ISBI), hosted by the international skin imaging collaboration (ISIC)

Cited by 1,550 publications

References 26 publications

Accuracy of a smartphone application for triage of skin lesions based on machine learning algorithms

Accuracy of a smartphone application for triage of skin lesions based on machine learning algorithms

A Novel Skin Lesion Detection Approach Using Neutrosophic Clustering and Adaptive Region Growing in Dermoscopy Images

Enhanced K-mean Using Evolutionary Algorithms for Melanoma Detection and Segmentation in Skin Images

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