Screening of COVID-19 Suspected Subjects Using Multi-Crossover Genetic Algorithm Based Dense Convolutional Neural Network

Abstract

Fast and accurate screening of novel coronavirus (COVID-19) suspected subjects plays a vital role in timely quarantine and medical care. Deep transfer learning-based screening models on chest X-ray (CXR) are effective for countering the COVID-19 outbreak. However, an efficient screening of COVID-19 is still a huge task due to the spatial complexity of CXRs. In this paper, a dense convolutional neural network (DCov-Net) based transfer learning model is proposed for the screening of COVID-19 suspected subjects using CXR images. A modified multi-crossover genetic algorithm (MMCGA) is then proposed to tune the hyper-parameters of DCov-Net. Majority of the existing COVID-19 diagnosis models are not interpretable as they do not provide any transparency to the users. Therefore, the concept of heat-maps is used to achieve explainability and interpretability. MMCGA based DCov-Net is implemented on a multiclass dataset that contains four different classes. Experimental results reveal that MMCGA based DCov-Net achieves better performance than the existing models. The proposed MMCGA based DCov-Net can be utilized for initial screening of COVID-19 suspected subjects with an accuracy of 99.34 ± 0.51 %.

Evaluating the Impact of COVID-19 on Society, Environment, Economy, and Education

“…This has led to an approximate 10-15% increase in research in the biomedical field [95]. On the other hand, artificial intelligence and machine learning researchers have been conducting extensive research studies with several published articles in various reputed journals [96][97][98][99][100]. Moreover, a large number of research articles on COVID-19 has been uploaded on preprint servers such as bioRxiv, medRxiv, and arXiv.…”

[Retracted] Software Systems Security Vulnerabilities Management by Exploring the Capabilities of Language Models Using NLP

“…The architecture has three sets of dense and dropout layers, with a dropout set at 25%. Binary cross-entropy loss [ 32 , 33 ] and Adam optimizer [ 34 ] are configured for model compilation. Model architecture is run on the training data set.…”

Ensemble Deep Learning and Internet of Things‐Based Automated COVID‐19 Diagnosis Framework

“…This fine-tuning strategy of deep CNN models has proved to be the most effective approach of transfer learning, which incrementally adapts the pretrained features to the new data [ 43 ]. Some recently utilized deep learning models are as ResNet152V2 [ 44 ], DenseNet201 [ 34 ], and Inception ResNetV2 (IRNV2) [ 3 ].…”

“…Although the existing deep learning models have achieved significantly better performance for COVID-19 diagnosis, still a majority of the deep learning models suffer from the overfitting problems [ 34 , 35 ]. Also, deep learning models have millions of parameters that are optimized using stochastic gradient descent algorithms [ 6 , 36 ].…”