Deep Learning, Sparse Coding, and SVM for Melanoma Recognition in Dermoscopy Images

Codella, Noel; Cai, Junjie; Abedini, Mani; Garnavi, Rahil; Halpern, Allan C.; Smith, J. R.

doi:10.1007/978-3-319-24888-2_15

Cited by 306 publications

(180 citation statements)

References 20 publications

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“…They obtained a high 93% of classification accuracy compared to other systems. However, in [23], the authors developed a new melanoma recognition system based on combined approach of deep learning, sparse coding, and support vector machine (SVM) learning algorithms. They have tested this approach on 2624 clinical cases of melanoma (334), atypical nevi (144), and benign lesions (2146).…”

Section: Review and Backgroundmentioning

confidence: 99%

“…In fact, those systems were devoted for differentiation between melanoma and nevus skin lesions using machine learning methods. As advised in [22][23][24], the outdated features and classification methods provided accuracy recognition of melanocytic or non-melanocytic PSLs at less than 90%. Moreover, the extraction of color and texture features is computationally expensive for previously developed systems.…”

Section: Review and Backgroundmentioning

confidence: 99%

“…Moreover, the extraction of color and texture features is computationally expensive for previously developed systems. Despite these facts, the deep learning algorithms [22][23][24] have also been used with bag-of-visual-words to combine local and global features of lesions. However, the deep learning algorithms have achieved very impressive results for solving the problems of image classification, and it has many applications in computer vision [25] and medical domain in practice.…”

Section: Review and Backgroundmentioning

confidence: 99%

See 2 more Smart Citations

Prediction of Dermoscopy Patterns for Recognition of both Melanocytic and Non-Melanocytic Skin Lesions

2016

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Abstract:A differentiation between all types of melanocytic and non-melanocytic skin lesions (MnM-SK) is a challenging task for both computer-aided diagnosis (CAD) and dermatologists due to the complex structure of patterns. The dermatologists are widely using pattern analysis as a first step with clinical attributes to recognize all categories of pigmented skin lesions (PSLs). To increase the diagnostic accuracy of CAD systems, a new pattern classification algorithm is proposed to predict skin lesions patterns by integrating the majority voting (MV-SVM) scheme with multi-class support vector machine (SVM). The optimal color and texture features are also extracted from each region-of-interest (ROI) dermoscopy image and then these normalized features are fed into an MV-SVM classifier to recognize seven classes. The overall system is evaluated using a dataset of 350 dermoscopy images (50 ROIs per class). On average, the sensitivity of 94%, specificity of 84%, 93% of accuracy and area under the receiver operating curve (AUC) of 0.94 are achieved by the proposed MnM-SK system compared to state-of-the-art methods. The obtained result indicates that the MnM-SK system is successful for obtaining the high level of diagnostic accuracy. Thus, it can be used as an alternative pattern classification system to differentiate among all types of pigmented skin lesions (PSLs).

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Section: Review and Backgroundmentioning

confidence: 99%

Section: Review and Backgroundmentioning

confidence: 99%

Section: Review and Backgroundmentioning

confidence: 99%

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Prediction of Dermoscopy Patterns for Recognition of both Melanocytic and Non-Melanocytic Skin Lesions

2016

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“…The melanoma diagnosis can be improved with the ABCD rule based and computer assisted systems. These systems usually consist of the separate units for the image segmentation, feature extraction and classification respectively [8][9][10][11][12]. Studies conducted in this field are as follows: Baldrick et al compared in their study the expert opinion and artificial neural networks when they classify the lesions.…”

Section: Introductionmentioning

confidence: 99%

Skin Lesion Classification using Machine Learning Algorithms

Özkan

Köklü

2017

ijisae

102

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Melanoma is a deadly skin cancer that breaks out in the skin's pigment cells on the skin surface. Melanoma causes 75% of the skin cancer-related deaths. This disease can be diagnosed by a dermatology specialist through the interpretation of the dermoscopy images in accordance with ABCD rule. Even if dermatology experts use dermatological images for diagnosis, the rate of the correct diagnosis of experts is estimated to be 75-84%. The purpose of this study is to pre-classify the skin lesions in three groups as normal, abnormal and melanoma by machine learning methods and to develop a decision support system that should make the decision easier for a doctor. The objective of this study is skin lesions based on dermoscopic images PH 2 datasets using 4 different machine learning methods namely; ANN, SVM, KNN and Decision Tree. Correctly classified instances were found as 92.50%, 89.50%, 82.00% and 90.00% for ANN, SVM, KNN and DT respectively. The findings show that the system developed in this study has the feature of a medical decision support system which can help dermatologists in diagnosing of the skin lesions.

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“…Machine learning solves classification problems by finding a fully good function, that is, the classifier, by use of a specific learning algorithm in the hypothetical space to simulate the actual classification function. The widely used single classifier models are forward feedback artificial (BP) neural network [40], support vector machine (SVM) [10] and decision tree [27]. BP neural network is a non-linear mapping system with a forward structure without a feedback one.…”

Section: Introductionmentioning

confidence: 99%

Multi-classifier ensemble based on dynamic weights

Ren

2017

Multimed Tools Appl

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In this study, a novel multi-classifier ensemble method based on dynamic weights is proposed to reduce the interference of unreliable decision information and improve the accuracy of fusion decision. The algorithm defines decision credibility to describe the realtime importance of the classifier to the current target, combines this credibility with the reliability calculated by the classifier on the training data set and dynamically assigns the fusion weight to the classifier. Compared with other methods, the contribution of different classifiers to fusion decision in acquiring weights is fully evaluated in consideration of the capability of the classifier to not only identify different sample regions but also output decision information when identifying specific targets. Experimental results on public face databases show that the proposed method can obtain higher classification accuracy than that of single classifier and some popular fusion algorithms. The feasibility and effectiveness of the proposed method are verified.

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Deep Learning, Sparse Coding, and SVM for Melanoma Recognition in Dermoscopy Images

Cited by 306 publications

References 20 publications

Prediction of Dermoscopy Patterns for Recognition of both Melanocytic and Non-Melanocytic Skin Lesions

Prediction of Dermoscopy Patterns for Recognition of both Melanocytic and Non-Melanocytic Skin Lesions

Skin Lesion Classification using Machine Learning Algorithms

Multi-classifier ensemble based on dynamic weights

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