Detecting COVID-19 from Lung Computed Tomography Images: A Swarm Optimised Artificial Neural Network Approach
IEEE Access
; : 2023/01/01 00:00:00.000, 2023.
Article
in English
| Scopus | ID: covidwho-2234580
ABSTRACT
COVID-19 has affected many people across the globe. Though vaccines are available now, early detection of the disease plays a vital role in the better management of COVID-19 patients. An Artificial Neural Network (ANN) powered Computer Aided Diagnosis (CAD) system can automate the detection pipeline accounting for accurate diagnosis, overcoming the limitations of manual methods. This work proposes a CAD system for COVID-19 that detects and classifies abnormalities in lung CT images using Artificial Bee Colony (ABC) optimised ANN (ABCNN). The proposed ABCNN approach works by segmenting the suspicious regions from the CT images of non-COVID and COVID patients using an ABC optimised region growing process and extracting the texture and intensity features from those suspicious regions. Further, an optimised ANN model whose input features, initial weights and hidden nodes are optimised using ABC optimisation classifies those abnormal regions into COVID and non-COVID classes. The proposed ABCNN approach is evaluated using the lung CT images collected from the public datasets. In comparison to other available techniques, the proposed ABCNN approach achieved a high classification accuracy of 92.37% when evaluated using a set of 470 lung CT images. Author
Artificial Bee Colony Algorithm; Artificial neural networks; Classification accuracy; Computed tomography; COVID-19; Feature extraction; Lung; Multilayer Perceptron; Pulmonary diseases; Resilient backpropagation; Solid modeling; Texture features; Biological organs; Computer aided diagnosis; Computerized tomography; Extraction; Neural networks; Optimization; Artificial bees; Bee colony algorithms; Features extraction; Multilayers perceptrons; Solid modelling
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Experimental Studies
Topics:
Vaccines
Language:
English
Journal:
IEEE Access
Year:
2023
Document Type:
Article
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