Deep features to classify skin lesions

@article{Kawahara2016DeepFT,
  title={Deep features to classify skin lesions},
  author={Jeremy Kawahara and A{\"i}cha Bentaieb and G. Hamarneh},
  journal={2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI)},
  year={2016},
  pages={1397-1400}
}
Diagnosing an unknown skin lesion is the first step to determine appropriate treatment. We demonstrate that a linear classifier, trained on features extracted from a convolutional neural network pretrained on natural images, distinguishes among up to ten skin lesions with a higher accuracy than previously published state-of-the-art results on the same dataset. Further, in contrast to competing works, our approach requires no lesion segmentations nor complex preprocessing. We gain consistent… Expand
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References

SHOWING 1-10 OF 16 REFERENCES
Deep Learning, Sparse Coding, and SVM for Melanoma Recognition in Dermoscopy Images
TLDR
An approach for melanoma recognition in dermoscopy images that combines deep learning, sparse coding, and support vector machine SVM learning algorithms is presented, suggesting the proposed approach is an effective improvement over prior state-of-art. Expand
A Color and Texture Based Hierarchical K-NN Approach to the Classification of Non-melanoma Skin Lesions
TLDR
This chapter proposes a novel hierarchical classification system based on the K-Nearest Neighbors (K-NN) model and its application to non-melanoma skin lesion classification that reaches an overall classification accuracy of 74 % over five common classes of skin lesions, including two non-Melanoma cancer types. Expand
Four-Class Classification of Skin Lesions With Task Decomposition Strategy
TLDR
A new computer-aided method to distinguish among melanomas, nevi, BCCs, and SKs is developed and specific features effective for the classification task including irregularity of color distribution are identified. Expand
Hierarchical Classification of Ten Skin Lesion Classes
TLDR
A hierarchical classification system based on the kNearest Neighbors (kNN) classifier for classification of ten different classes of Malignant and Benign skin lesions from color image data is presented. Expand
ImageNet classification with deep convolutional neural networks
TLDR
A large, deep convolutional neural network was trained to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes and employed a recently developed regularization method called "dropout" that proved to be very effective. Expand
Computerized analysis of pigmented skin lesions: A review
TLDR
An extensive introduction to and clarify ambiguities in the terminology used in the literature is provided to simplify literature searches on a specific sub-topic and an extended categorization of PSL feature descriptors is proposed, associating them with the specific methods for diagnosing melanoma. Expand
DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
TLDR
DeCAF, an open-source implementation of deep convolutional activation features, along with all associated network parameters, are released to enable vision researchers to be able to conduct experimentation with deep representations across a range of visual concept learning paradigms. Expand
Very Deep Convolutional Networks for Large-Scale Image Recognition
TLDR
This work investigates the effect of the convolutional network depth on its accuracy in the large-scale image recognition setting using an architecture with very small convolution filters, which shows that a significant improvement on the prior-art configurations can be achieved by pushing the depth to 16-19 weight layers. Expand
Depth Data Improves Skin Lesion Segmentation
This paper shows that adding 3D depth information to RGB colour images improves segmentation of pigmented and non-pigmented skin lesion. A regionbased active contour segmentation approach using aExpand
Depth Data Improves Skin Lesion Segmentation
This paper shows that adding 3D depth information to RGB colour images improves segmentation of pigmented and non-pigmented skin lesion. A region-based active contour segmentation approach using aExpand
...
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...