The detection of recorded epileptic seizure activity in electroencephalogram (EEG) segments is crucial for the classification of seizures. Manual recognition is a time-consuming and laborious process that places a heavy burden on neurologists, and hence, the automatic identification of epilepsy has become an important issue. Traditional EEG recognition models largely depend on artificial experience and are of weak generalization ability. To break these limitations, we propose a novel one-dimensional deep neural network for robust detection of seizures, which composes of three convolutional blocks and three fully connected layers. Thereinto, each convolutional block consists of five types of layers: convolutional layer, batch normalization layer, nonlinear activation layer, dropout layer, and max-pooling layer. Model performance is evaluated on the University of Bonn dataset, which achieves the accuracy of 97.63%∼99.52% in the two-class classification problem, 96.73%∼98.06% in the three-class EEG classification problem, and 93.55% in classifying the complicated five-class problem.
Lung cancer is one of the highest causes of cancer-related death in both men and women. Therefore, various diagnostic methods for lung nodules classification have been proposed to implement the early detection. Due to the limited amount and diversity of samples, these methods encounter some bottlenecks. In this paper, we intend to develop a method to enlarge the dataset and enhance the performance of pulmonary nodules classification. We propose a data augmentation method based on generative adversarial network (GAN), called Forward and Backward GAN (F&BGAN), which can generate high-quality synthetic medical images. F&BGAN has two stages, Forward GAN (FGAN) generates diverse images, and Backward GAN (BGAN) is used to improve the quality of images. Besides, a hierarchical learning framework, multi-scale VGG16 (M-VGG16) network, is proposed to extract discriminative features from alternating stacked layers. The methodology was evaluated on the Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) dataset, with the best accuracy of 95.24%, sensitivity of 98.67%, specificity of 92.47% and area under ROC curve (AUROC) of 0.980. Experimental results demonstrate the feasibility of F&BGAN in generating medical images and the effectiveness of M-VGG16 in classifying malignant and benign nodules.
Background/Aims:Clinical studies have shown that hyperuricaemia is strongly associated with cardiovascular disease. However, the molecular mechanisms of high uric acid (HUA) associated with cardiovascular disease remain poorly understood. In this study, we investigated the effect of HUA on cardiomyocytes. Methods: We exposed H9c2 cardiomyocytes to HUA, then cell viability was determined by MTT assay, and reactive oxygen species' (ROS) production was detected by a fluorescence assay. Western blot analysis was used to examine phosphorylation of extracellular signal-regulated kinase (ERK), p38, phosphatidylinositol 3-kinase (PI3K) and Akt. We monitored the impact of HUA on phospho-ERK and phospho-p38 levels in myocardial tissue from an acute hyperuricaemia mouse model established by potassium oxonate treatment. Results: HUA decreased cardiomyocyte viability and increased ROS production in cardiomyocytes; pre-treatment with N-acetyl-L-cysteine, a ROS scavenger, and PD98059, an ERK inhibitor, reversed HUA-inhibited viability of cardiomyocytes. Further examination of signal transduction pathways revealed HUA-induced ROS involved in activating ERK/P38 and inhibiting PI3K/Akt in cardiomyocytes. Furthermore, the acute hyperuricaemic mouse model showed an increased phospho-ERK/p38 level in myocardial tissues. Conclusion: HUA induced oxidative damage and inhibited the viability of cardiomyocytes by activating ERK/p38 signalling, for a novel potential mechanism of hyperuricaemic-related cardiovascular disease.
The automatic segmentation of the skin lesion on dermoscopy images is an important step for diagnosing the melanoma. However, the skin lesion segmentation is still a challenging task due to the blur lesion border, low contrast between the skin cancer region and normal tissue background, and various sizes of cancer regions. In this paper, we propose a deep supervised multi-scale network (DSM-Network), which achieves satisfied skin cancer segmentation result by utilizing the side-output layers of the network to aggregate information from shallow&deep layers, and designing a multi-scale connection block to handle a variety of cancer sizes' changes. Moreover, a post-processing of the contour refinement strategy is adopted by a conditional random field (CRF) model to further improve the segmentation results. Extensive experiments on two public datasets: ISBI 2017 and PH2 have demonstrated that our designed DSM-Network has gained competitive performance compared with other state-of-the-art methods.INDEX TERMS Skin cancer, dermoscopy image, deep supervised learning, multi-scale feature, conditional random field.
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