Conditional GAN based augmentation for predictive modeling of respiratory signals

Abstract

Respiratory illness is the primary cause of mortality and impairment in the life span of an individual in the current COVID–19 pandemic scenario. The inability to inhale and exhale is one of the difficult conditions for a person suffering from respiratory disorders. Unfortunately, the diagnosis of respiratory disorders with the presently available imaging and auditory screening modalities are sub-optimal and the accuracy of diagnosis varies with different medical experts. At present, deep neural nets demand a massive amount of data suitable for precise models. In reality, the respiratory data set is quite limited, and therefore, data augmentation (DA) is employed to enlarge the data set. In this study, conditional generative adversarial networks (cGAN) based DA is utilized for synthetic generation of signals. The publicly available repository such as ICBHI 2017 challenge, RALE and Think Labs Lung Sounds Library are considered for classifying the respiratory signals. To assess the efficacy of the artificially created signals by the DA approach, similarity measures are calculated between original and augmented signals. After that, to quantify the performance of augmentation in classification, scalogram representation of generated signals are fed as input to different pre-trained deep learning architectures viz Alexnet, GoogLeNet and ResNet-50. The experimental results are computed and performance results are compared with existing classical approaches of augmentation. The research findings conclude that the proposed cGAN method of augmentation provides better accuracy of 92.50% and 92.68%, respectively for both the two data sets using ResNet 50 model.

An Improved COVID-19 Detection using GAN-Based Data Augmentation and Novel QuNet-Based Classification

“…At the peak of this pandemic, a lot of research work [ 5 , 6 ] has been done as well as in progress to help ease in detecting and treating COVID-19 [ 7 , 8 ]. With an inclination towards using deep learning models [ 9 , 10 ] for detection of corona virus, various papers have proposed and showed how to use CNNs and transfer learning to use VGG16, VGG19, ResNet, DenseNet, and other models for fine-tuning and feature extraction in currently available X-ray and CT scan dataset.…”

Intelligent speech technologies for transcription, disease diagnosis, and medical equipment interactive control in smart hospitals: A review

“…A crucial reason is that the data is difficult to meet the needs of state-of-the-art end-to-end recognition methods. Therefore, some studies have tried solutions such as data augmentation [ 139 , 140 , 149 , 171 ] and transfer learning [ 141 , 145 , 149 ] to solve this problem. The details of the diagnosis systems in these studies, including data sources, voice type, voice feature, classifier, and effect, can be found in Table 4 .…”

DualDiscWaveGAN-Based Data Augmentation Scheme for Animal Sound Classification

“…This problem can be addressed by using conditional GANs that generate multiple class data within one unified model. For instance, Jayalakshmy et al [ 21 ] used a conditional GAN for respiratory waveform augmentation. They combined a 1D GAN with a standard conditional GAN (cGAN) [ 22 ], whose generator and discriminator receive conditions via embedding layers and concatenation operations.…”