As an epidemic, COVID-19’s core test instrument still has serious flaws. To improve the present condition, all capabilities and tools available in this field are being used to combat the pandemic. Because of the contagious characteristics of the unique coronavirus (COVID-19) infection, an overwhelming comparison with patients queues up for pulmonary X-rays, overloading physicians and radiology and significantly impacting the quality of care, diagnosis, and outbreak prevention. Given the scarcity of clinical services such as intensive care and motorized ventilation systems in the aspect of this vastly transmissible ailment, it is critical to categorize patients as per their risk categories. This research describes a novel use of the deep convolutional neural network (CNN) technique to COVID-19 illness assessment seriousness. Utilizing chest X-ray images as contribution, an unsupervised DCNN model is constructed and suggested to split COVID-19 individuals into four seriousness classrooms: low, medium, serious, and crucial with an accuracy level of 96 percent. The efficiency of the DCNN model developed with the proposed methodology is demonstrated by empirical findings on a suitably huge sum of chest X-ray scans. To the evidence relating, it is the first COVID-19 disease incidence evaluation research with four different phases, to use a reasonably high number of X-ray images dataset and a DCNN with nearly all hyperparameters dynamically adjusted by the variable selection optimization task.
Corona virus Disease (COVID-19) is an acute pandemic which has put the lives of millions of people worldwide at risk during recent times. There is a high demand to develop effective tools and methods to diagnose the COVID infection in people at an early stage to prevent the spread of the disease to a larger community. This paper aims to provide a systematic method for COVID diagnosis using machine learning and deep learning algorithms. The proposed method Hybrid Deep Recurrent Neural Network (HDRNN) is a fusion of Convolution Neural Networks (CNN) and Long Short-Term Memory-Recurrent Neural Networks (LSTM-RNN) to detect COVID infection efficiently from X-ray samples. CNN is employed in the proposed method primarily to extract the essential features from the X-ray images and LSTM is suitable to classify the COVID affected patients with more fidelity. The dataset used in this work consists of an aggregate of 3470 images including COVID affected and Pneumonia affected samples. The experimental results carried out on the collected dataset with the proposed HDRNN method demonstrated an accuracy of 99.4%, F1 Score 98.7%, Sensitivity of 99.3% and Specificity of 99.2 %.
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