Background and Objective. Skin lesion classification by using deep learning technologies is still a considerable challenge due to high similarity among classes and large intraclass differences, serious class imbalance in data, and poor classification accuracy with low robustness. Approach. To address these issues, a two-stage framework for dermoscopy lesion classification using adversarial training and a fuzzy rank-based ensemble of multilayer feature fusion convolutional neural network (CNN) models is proposed. In the first stage, dermoscopy dataset augmentation based on generative adversarial networks is proposed to obtain realistic dermoscopy lesion images, enabling significant improvement for balancing the number of lesions in each class. In the second stage, a fuzzy rank-based ensemble of multilayer feature fusion CNN models is proposed to classify skin lesions. In addition, an efficient channel integrating spatial attention module, in which a novel dilated pyramid pooling structure is designed to extract multiscale features from an enlarged receptive field and filter meaningful information of the initial features. Combining the cross-entropy loss function with the focal loss function, a novel united loss function is designed to reduce the intraclass sample distance and to focus on difficult and error-prone samples to improve the recognition accuracy of the proposed model. Main results. In this paper, the common dataset (HAM10000) is selected to conduct simulation experiments to evaluate and verify the effectiveness of the proposed method. The subjective and objective experimental results demonstrate that the proposed method is superior over the state-of-the-art methods for skin lesion classification due to its higher accuracy, specificity and robustness. Significance. The proposed method effectively improves the classification performance of the model for skin diseases, which will help doctors make accurate and efficient diagnoses, reduce the incidence rate and improve the survival rates of patients.

Dermoscopy lesion classification based on GANs and a fuzzy rank-based ensemble of CNN models

Image segmentation means subdividing the image into different objects. We use different methods for the segmentation of images. For getting different objects from a single image, we generally apply old methods, such as Fuzzy C-means, and K-means clustering techniques. In the case of getting different objects from huge amounts of image datasets, we generally apply the CNN technique. The Activation Function (AF) plays a major role during the segmentation of images through CNN. We can increase the power of CNN using activation functions. Generally, the exponential activation function is used for very complex and non-separable types of problems. So, in this paper, we segment the triangle and CamVid datasets on CNN using NewSigmoid AF and root2Sigmoid AF. Both AFs are exponential activation functions. We made a 12-layer CNN for the triangle segment and a 13-layer CNN for the CamVid segment. After that, we compare four activation functions on both CNNs. These activation functions are NewSigmoid, Relu, root2sigmoid, and Tanh. On the triangle image dataset, we have achieved global accuracy of 96.29% through NewSigmoid AF, 95.77% through Relu AF, 97.02% through root2sigmoid AF, and 96.47% through Tanh AF. On the CamVid dataset, we have achieved global accuracy of 93.97% through NewSigmoid AF, 94.01% through Relu AF, 94.19% through root2sigmoid AF, and 94.13% through Tanh AF. We also check the validation accuracy of the CamVid dataset. We take 10% of images for validation purpose. For the same dataset and same CNN network, we found validation accuracy of relu is 96.04%, root2simgoid is 96.26%, tanh is 96.20% and NewSimgois is 96.26%. Therefore, we can say that the root2sigmoid AF gives a better result as compared with NewSigmoid, Relu, and Tanh AFs. 

Image Segmentation on Convolutional Neural Network (CNN) using Some New Activation Functions

At present, most of the research methods of image semantic segmentation are based on Fully Convolutional Networks (FCN). However, FCN will cause the loss of image feature information when performing image semantic segmentation, and the details of the output image will not be processed well. Therefore, we propose to take the ResNet network as the encoder basic network. Using dilated convolution to extract context information, and designing a multi-scale feature fusion method in the decoder to make full use of features from each level to enrich representative ability of feature points, so that it can classify image pixels well. Extensive experiments demonstrate that our method shows superior performance over other methods on the PASCAL VOC2012 [10]validation dataset.

Image Semantic Segmentation Based on Dilated Convolution and Multi-Layer Feature Fusion

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Image Semantic Segmentation Based on Dilated Convolution and Multi-Layer Feature Fusion