Wavelet and deep learning-based detection of SARS-nCoV from thoracic X-ray images for rapid and efficient testing

Abstract

This paper proposes a wavelet and artificial intelligence-enabled rapid and efficient testing procedure for patients with Severe Acute Respiratory Coronavirus Syndrome (SARS-nCoV) through a deep learning approach from thoracic X-ray images. Presently, the virus infection is diagnosed primarily by a process called the real-time Reverse Transcriptase- Polymerase Chain Reaction (rRT-PCR) based on its genetic prints. This whole procedure takes a substantial amount of time to identify and diagnose the patients infected by the virus. The proposed research uses a wavelet-based convolution neural network architectures to detect SARS-nCoV. CNN is pre-trained on the ImageNet and trained end-to-end using thoracic X-ray images. To execute Discrete Wavelet Transforms (DWT), the available mother wavelet functions from different families, namely Haar, Daubechies, Symlet, Biorthogonal, Coiflet, and Discrete Meyer, were considered. Two-level decomposition via DWT is adopted to extract prominent features peripheral and subpleural ground-glass opacities, often in the lower lobes explicitly from thoracic X-ray images to suppress noise effect, further enhancing the signal to noise ratio. The proposed wavelet-based deep learning models of both, two-class instances (COVID vs. Normal) and four-class instances (COVID-19 vs. PNA bacterial vs. PNA viral vs. Normal) were validated from publicly available databases using k-Fold Cross Validation (k-Fold CV) technique. In addition to these X-ray images, images of recent COVID-19 patients were further used to examine the model’s practicality and real-time feasibility in combating the current pandemic situation. It was observed that the Symlet 7 approximation component with two-level manifested the highest test accuracy of 98.87%, followed by Biorthogonal 2.6 with an efficiency of 98.73%. While the test accuracy for Symlet 7 and Biorthogonal 2.6 is high, Haar and Daubechies with two levels have demonstrated excellent validation accuracy on unseen data. It was also observed that the precision, the recall rate, and the dice similarity coefficient for four-class instances were 98%, 98%, and 99%, respectively, using the proposed algorithm.

A Deep Learning and Handcrafted Based Computationally Intelligent Technique for Effective COVID-19 Detection from X-ray/CT-scan Imaging

“…Before the classification step, the subset of features is reduced using principal component analysis (PCA). Similarly, in another study [30] the authors detect COVID-19 using a wavelet-based convolution neural network. They considered different wavelet families like discrete Meyer, Coiflt, Biorthogonal, Symlet, Daubechies, and Haar to execute DWT.…”

WRANet: wavelet integrated residual attention U-Net network for medical image segmentation

“…Liu et al [ 40 ] proposed a novel multilevel wavelet convolutional neural network model (MWCNN), which introduced wavelet transform to reduce the size of feature images and reconstructed high-resolution feature images using inverse wavelet transform. Verma et al [ 41 ] proposed a wavelet-based convolutional neural network architecture to detect SARS-NCOV, using mother wavelet functions from different families to perform discrete wavelet transform (DWT) and two-stage DWT decomposition to suppress the noise in chest X-ray images. Kang et al [ 42 ] proposed a residual wavelet network, which synergistically combined the expression ability of deep learning with the denoising performance of the wavelet framework.…”

Automated Detection of Lung Diseases (covid-19) Based on X-ray Images Using A Deep Learning Approach

“…A popular activation function that generates a vector of probability distributions for various classes is called softmax activation. To add nonlinearity to the model, activation functions are utilized (Verma et al, 2021).…”