Melanoma Recognition in Dermoscopy Images via Aggregated Deep Convolutional Features -Deep learning project-Matlab

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Melanoma Recognition in Dermoscopy Images via Aggregated Deep Convolutional Features -Deep learning project-Matlab

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Melanoma Recognition in Dermoscopy Images via Aggregated Deep Convolutional Features -Deep learning project-Matlab

In this project, we present a novel framework for dermoscopy image recognition via both a deep learning method and a local descriptor encoding strategy. Specifically, deep representations of a rescaled dermoscopy image are first extracted via a very deep residual neural network pretrained on a large natural image dataset. Then these local deep descriptors are aggregated by orderless visual statistic features based on Fisher vector (FV) encoding to build a global image representation. Finally, the FV encoded representations are used to classify melanoma images using a convolutional neural network. Our proposed method is capable of generating more discriminative features to deal with large variations within melanoma classes, as well as small variations between melanoma and nonmelanoma classes with limited training data. Extensive experiments are performed to demonstrate the effectiveness of our proposed method. 

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