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| Melanoma Recognition in Dermoscopy Images via Deep Residual Network |
| Li Hang, Yu Zhen, Ni Dong, Lei Baiying, Wang Tianfu* |
| Department of Biomedical Engineering, School of Medicine, Shenzhen University, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, Shenzhen 518060, Guangdong, China |
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Abstract Malignant melanoma is one of the most common and deadly skin cancers. Clinically, dermoscopy is a routine method for early diagnosis of malignant melanoma. However, human's visual examinations are laborious, time-consuming, and highly dependent on dermatologist’s clinical experience. Therefore, it is important to design an algorithm for recognizing melanoma automatically in dermoscopy images. This study proposed a novel framework for the evaluation of dermoscopy images, using deep learning to generate more discriminative features with limited training data. Specifically, we first extracted the intermediate convolutional features of each skin lesion image using a very deep residual neural network including 152 network layers (i.e. Res-152) which was pre-trained on a large natural image dataset, and the final deep representation was obtained by averaging the spatial feature maps into single feature vector, then, the support vector machine (SVM) was used to classify the melanoma. By using the proposed method 248 melanoma images and 1031 non-melanoma images in published ISBI 2016 challenge datasets of skin lesion images were evaluated, obtaining accuracy rate of 84.96% and AUC of 84.18%. In addition, in order to demonstrate the effect of neural network depth on the classification results, we compared the different depth of the model framework. Our approach, which could solve large variations in melanoma and small differences between melanoma and non-melanoma with the limited training data, can produce more discriminative representations than existing studies using hand-crafted features (i.e. the BoF models based on densely sampled SIFT (DSIFT) descriptors) or only to extract features from the fully connected layers.
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Received: 21 July 2017
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