WOANet: Whale optimized deep neural network for the classification of COVID-19 from radiography images

Abstract

Coronavirus Diseases (COVID-19) is a new disease that will be declared a global pandemic in 2020. It is characterized by a constellation of traits like fever, dry cough, dyspnea, fatigue, chest pain, etc. Clinical findings have shown that the human chest Computed Tomography(CT) images can diagnose lung infection in most COVID-19 patients. Visual changes in CT scan due to COVID-19 is subjective and evaluated by radiologists for diagnosis purpose. Deep Learning (DL) can provide an automatic diagnosis tool to relieve radiologists’ burden for quantitative analysis of CT scan images in patients. However, DL techniques face different training problems like mode collapse and instability. Deciding on training hyper-parameters to adjust the weight and biases of DL by a given CT image dataset is crucial for achieving the best accuracy. This paper combines the backpropagation algorithm and Whale Optimization Algorithm (WOA) to optimize such DL networks. Experimental results for the diagnosis of COVID-19 patients from a comprehensive COVID-CT scan dataset show the best performance compared to other recent methods. The proposed network architecture results were validated with the existing pre-trained network to prove the efficiency of the network.

Jaya‐tunicate swarm algorithm based generative adversarial network for COVID‐19 prediction with chest computed tomography images

“…The data that support the findings of this study are openly available in UCSD-AI4H/COVID-CT dataset at https://github.com/UCSD-AI4H/COVID-CT, reference number [27] and DeepCovid dataset at https://github.com/shervinmin/DeepCovid, reference number [35].…”

“…The efficacy is analyzed by considering the presented methods, like deep transfer learning, 9 Prior‐attention residual learning (PARL), 20 deep learning, 6 Lesion‐attention deep neural network (LA‐DNN), 10 WOA‐based GAN, 32 E‐DiCoNet, 33 multi‐COVID‐Net, 34 WOANet, 35 GSO‐based GAN, and GAN.…”

An automated diagnosis and classification of COVID-19 from chest CT images using a transfer learning-based convolutional neural network

“…Murugan et al [ 25 ] proposed an optimized DL network (WOANet)for feature extraction and binary classification of COVID-19. They used the ResNet-50 CNN network to diagnose the COVID-19 through CCT images.…”

COVIDnet: An Efficient Deep Learning Model for COVID-19 Diagnosis on Chest CT Images

“…The 746 chest CT scans were trained using 147 million parameters and three distinct learning techniques using the EfficientNet model [13], which achieved an accuracy of 89.7%. For COVID-19 feature extraction and binary classification, an optimized DL network Whale Optimization Algorithm (WOANet) [14] was used. They employed CCT pictures to diagnose COVID-19 using the ResNet-50 CNN network.…”