Microscopic segmentation and classification of COVID-19 infection with ensemble convolutional neural network.
Microsc Res Tech
; 85(1): 385-397, 2022 Jan.
Article
in English
| MEDLINE | ID: covidwho-1372740
ABSTRACT
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.
Keywords
Full text:
Available
Collection:
International databases
Database:
MEDLINE
Main subject:
COVID-19
Type of study:
Diagnostic study
/
Experimental Studies
Limits:
Humans
Language:
English
Journal:
Microsc Res Tech
Journal subject:
Diagnostic Imaging
Year:
2022
Document Type:
Article
Affiliation country:
Jemt.23913
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