A novel hand-crafted with deep learning features based fusion model for COVID-19 diagnosis and classification using ches
- PDF / 3,079,593 Bytes
- 17 Pages / 595.276 x 790.866 pts Page_size
- 26 Downloads / 183 Views
ORIGINAL ARTICLE
A novel hand-crafted with deep learning features based fusion model for COVID-19 diagnosis and classification using chest X-ray images K. Shankar1
· Eswaran Perumal1
Received: 7 August 2020 / Accepted: 6 October 2020 © The Author(s) 2020
Abstract COVID-19 pandemic is increasing in an exponential rate, with restricted accessibility of rapid test kits. So, the design and implementation of COVID-19 testing kits remain an open research problem. Several findings attained using radio-imaging approaches recommend that the images comprise important data related to coronaviruses. The application of recently developed artificial intelligence (AI) techniques, integrated with radiological imaging, is helpful in the precise diagnosis and classification of the disease. In this view, the current research paper presents a novel fusion model hand-crafted with deep learning features called FM-HCF-DLF model for diagnosis and classification of COVID-19. The proposed FM-HCF-DLF model comprises three major processes, namely Gaussian filtering-based preprocessing, FM for feature extraction and classification. FM model incorporates the fusion of handcrafted features with the help of local binary patterns (LBP) and deep learning (DL) features and it also utilizes convolutional neural network (CNN)-based Inception v3 technique. To further improve the performance of Inception v3 model, the learning rate scheduler using Adam optimizer is applied. At last, multilayer perceptron (MLP) is employed to carry out the classification process. The proposed FM-HCF-DLF model was experimentally validated using chest X-ray dataset. The experimental outcomes inferred that the proposed model yielded superior performance with maximum sensitivity of 93.61%, specificity of 94.56%, precision of 94.85%, accuracy of 94.08%, F score of 93.2% and kappa value of 93.5%. Keywords COVID-19 · Convolutional neural network · Preprocessing · Feature extraction · Fusion model · Classification
Introduction Coronavirus belongs to a huge family of viruses, which generally cause mild-to-moderate upper-respiratory tract illness similar to cold, namely Middle East respiratory syndrome (MERS) and Severe Acute Respiratory Syndrome (SARS) [1]. These illnesses generally occur in a wide range of animal species; however, in diverse cases, they tend to mutate and infect human beings quickly and spread to other people in an easier way. By the end of 2019, coronavirus 2019 (COVID19, acronym of COronaVIrus Disease 19) started infecting human beings. The first case was identified by December
B
Eswaran Perumal [email protected] K. Shankar [email protected]
1
Department of Computer Applications, Alagappa University, Karaikudi, India
2019 in Wuhan city, China which rapidly spread across the globe. Till now, there is a rapid evolution observed in coronavirus from 28 January 2020. By 15 February 2020, there were around 4600 COVID-19 affected cases globally with 160 mortalities. As of 22 September 2020, the total number of cases diagnosed is 31 million
Data Loading...
