We propose two simple and effective spiking neuron models to improve the response time of the conventional spiking neural network. The proposed neuron models adaptively tune the presynaptic input current depending on ...
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This paper presents an end-to-end ECG signal classification method based on a novel segmentation strategy via 1D Convolutional Neural networks (CNN) to aid the classification of ECG signals. The ECG segmentation strat...
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Artificial intelligence (AI) has rapidly developed through advancements in computational power and the growth of massive datasets. However, this progress has also heightened challenges in interpreting the "black-...
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Aimed at the problem that traditional histogram is sensitive to illumination changes in visual tracking, combined with the CN(Color Name) feature, we proposed a new feature(denotes CNH, Color Name Histogram) based on ...
Aimed at the problem that traditional histogram is sensitive to illumination changes in visual tracking, combined with the CN(Color Name) feature, we proposed a new feature(denotes CNH, Color Name Histogram) based on color name. Firstly, the method projected the original RGB image to CN space to obtain robust 11 feature layers. Then, we counted the each pixel numbers of feature layers. Finally, normalizing the amount of pixels in each layer. In addition, we adopted a feature adaptive fusion method to combine CNH and HOG(Histogram of Oriented Gradient). In order to prove validity of the proposed algorithm, we use Staple(Sum of Template And Pixel-wise Learners) algorithm frame to make a controlled trial. In contrast with the reference algorithms, the success of our algorithm increases by 1.5% and the precision increases by 1.7%. The results show that this method retains the advantages of traditional histogram which is insensitive to target deformation, but also enhances the robustness to illumination change.
The early screening and diagnosis of tuberculosis plays an important role in the control and treatment of tuberculosis infections. In this paper, an integrated computer-aided system based on deep learning is proposed ...
The early screening and diagnosis of tuberculosis plays an important role in the control and treatment of tuberculosis infections. In this paper, an integrated computer-aided system based on deep learning is proposed for the detection of multiple categories of tuberculosis lesions in chest radiographs. In this system, the fully convolutional neural network method is used to segment the lung area from the entire chest radiograph for pulmonary tuberculosis detection. Different from the previous analysis of the whole chest radiograph, we focus on the specific tuberculosis lesion areas for the analysis and propose the first multicategory tuberculosis lesion detection method. In it, a learning scalable pyramid structure is introduced into the Faster Region-based Convolutional network (Faster RCNN), which effectively improves the detection of small-area lesions, mines indistinguishable samples during the training process, and uses reinforcement learning to reduce the detection of false-positive lesions. To compare our method with the current tuberculosis detection system, we propose a classification rule for whole chest X-rays using a multicategory tuberculosis lesion detection model and achieve good performance on two public datasets (Montgomery: AUC = 0.977 and accuracy = 0.926; Shenzhen: AUC = 0.941 and accuracy = 0.902). Our proposed computer-aided system is superior to current systems that can be used to assist radiologists in diagnoses and public health providers in screening for tuberculosis in areas where tuberculosis is endemic.
Reconfigurable intelligent surface (RIS) constitutes an essential and promising paradigm that relies programmable wireless environment and provides capability for space-intensive communications, due to the use of low-...
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