There are two main problems about pneumothorax detection in X-ray images. The pneumothorax usually overlaps with tissues such as ribs and clavicles in X-ray images, easily causing missed diagnosis and the performance of the existing pneumothorax detection methods remain to be improved. The suspicious pneumothorax area detection cannot be exploited by the convolutional neural network-based algorithms, lacking the interpretability. Aiming at the problems, a novel method combining Dense convolutional Network (DenseNet) and gradient-weighted class activation mapping was proposed. Firstly, a large-scale chest X-ray dataset named PX-ray was constructed for model training and testing. Secondly, the output node of the DenseNet was modified and a sigmoid function was added after the fully connected layer to classify the chest X-ray images. In the training process, the weight of cross entropy loss function was set to alleviate the problem of data imbalance and improve the accuracy of the model. Finally, the parameters of the last convolutional layer of the network and the corresponding gradients were extracted, and the areas of the pneumothorax type were roughly located by gradient-weighted class activation mapping. The experimental results show that, the proposed method has the detection accuracy of 95.45%, and has the indicators such as Area Under Curve (AUC), sensitivity, specificity all higher than 0.9, performs the classic algorithms of VGG19, GoogLeNet and ResNet, and realizes the visualization of pneumothorax area.

According to the poor applicability and poor prediction accuracy fluctuation of the existing Software Reliability Growth Model (SRGM), this paper proposed a model based on Fuzzy Neural Network (FNN) which was connected with self-Adaptive Step Cuckoo Search (ASCS) algorithm, the weights and thresholds of the FNN were optimized by ASCS algorithm, then the FNN was used to establish SRGM. Software defect data were used in the FNNs training process, the weights and thresholds of FNN were adjusted by ASCS, the accuracy of prediction process was improved correspondingly, at the same time, in order to reduce the fluctuation of prediction by FNN, averaging method was used to deal with predicted results. Based on those, SRGM was established by self-Adaptive Step Cuckoo Search algorithm—Fuzzy Neural Network (ASCS-FNN). According to 3 groups of software defect data, taking Average Error (AE) and Sum of Squared Error (SSE) as measurements, the SRGMs one-step forward predictive ability established by ASCS-FNN was compared with the SRGMs one-step forward predictive ability established by Simulated Annealing—Dynamic Fuzzy Neural Network (SA-DFNN), FNN and Back Propagation Network (BPN). The simulation results confirm that, the SRGM based on ASCS-FNN relative to the SRGM based on SA-DFNN, FNN and BPN, the mean of Relative Increase (RI) of prediction accuracy rate for RI (AE) is -1.48%, 54.8%, 33.8%, and the mean of Relative Increase (RI) of prediction accuracy rate for RI (SSE) is 14.4%, 76%, 35.9%. The prediction of SRGM established by ASCS-FNN is more steadily than the prediction of SRGM established by FNN and BPN, and the net structure of ASCS-FNN is much simpler than the net structure of SA-DFNN, so the SRGM established by ASCS-FNN has high prediction accuracy, prediction stability, and some adaptability.

As weak bioelectricity signals, diaphragmatic electromyographic (EMGdi) signals are always corrupted by strong electrocardiography (ECG) signals. A denoising algorithm based on wavelet scalogram adaptive threshold was proposed in this paper to improve the precision of threshold in EMGdi signal denoising. This algorithm found the position of ECG interference by performing wavelet transform on the EMGdi signals and conveying wavelet coefficients to wavelet scalogram, and then automatically adjusted the threshold by ECG neighborhood wavelet energy in order to remove ECG interference. By comparing the results with the wavelet threshold, it shows that the proposed method can eliminate the ECG interference from EMGdi and reserve EMGdi signal characteristics effectively.