期刊
MICROSCOPY RESEARCH AND TECHNIQUE
卷 85, 期 1, 页码 385-397出版社
WILEY
DOI: 10.1002/jemt.23913
关键词
Deeplabv3; denoise convolutional neural network (DnCNN); healthcare; public health; ResNet-18; stack sparse autoencoder deep learning model (SSAE)
A three-phase model is proposed for COVID-19 detection in CT images, achieving a global segmentation accuracy of 0.96 and 0.97 for classification through denoising, segmentation, and deep learning processes.
The detection of biological RNA from sputum has a comparatively poor positive rate in the initial/early stages of discovering COVID-19, as per the World Health Organization. It has a different morphological structure as compared to healthy images, manifested by computer tomography (CT). COVID-19 diagnosis at an early stage can aid in the timely cure of patients, lowering the mortality rate. In this reported research, three-phase model is proposed for COVID-19 detection. In Phase I, noise is removed from CT images using a denoise convolutional neural network (DnCNN). In the Phase II, the actual lesion region is segmented from the enhanced CT images by using deeplabv3 and ResNet-18. In Phase III, segmented images are passed to the stack sparse autoencoder (SSAE) deep learning model having two stack auto-encoders (SAE) with the selected hidden layers. The designed SSAE model is based on both SAE and softmax layers for COVID19 classification. The proposed method is evaluated on actual patient data of Pakistan Ordinance Factories and other public benchmark data sets with different scanners/mediums. The proposed method achieved global segmentation accuracy of 0.96 and 0.97 for classification.
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