Research Article |
Ensemble Deep Convolution Neural Network for Sars-Cov-V2 Detection
Author(s) : Subrat Sarangi1, Uddeshya Khanna2 and Rohit Kumar3
Published In : International Journal of Electrical and Electronics Research (IJEER) Volume 10, Issue 3, Special Issue on Recent Advancements in the Electrical & Electronics Engineering
Publisher : FOREX Publication
Published : 10 August 2022
e-ISSN : 2347-470X
Page(s) : 481-486
Abstract
The continuing Covid-19 pandemic, caused by the SARS-CoV2 virus, has attracted the eye of researchers and many studies have focussed on controlling it. Covid-19 has affected the daily life, employment, and health of human beings along with socio-economic disruption. Deep Learning (DL) has shown great potential in various medical applications in the past decade and continues to assist in effective medical image analysis. Therefore, it is effectively being utilized to explore its potential in controlling the pandemic. Chest X-Ray (CXR) images were used in studies pertaining to DL for medical image analysis. With the burgeoning of Covid-19 cases by day, it becomes imperative to effectively screen patients whose CXR images show a tendency of Covid-19 infection. Several innovative Convolutional Neural Network (CNN) models have been proposed so far for classifying medical CXR images. Moreover, some studies used a transfer learning (TL) approach on state-of-art CNN models for the classification task. In this paper, we do a comparative study of these CNN models and TL approaches on state-of-art CNN models and have proposed an ensemble Deep Convolution Neural Network model (DCNN)
Keywords: Convolutional Neural Network (CNN)
, Deep Convolutional Neural Network (DCNN)
, DenseNet121
, VGG19
, Support Vector Machine (SVM)
Subrat Sarangi, Department of Applied Mathematics, Delhi Technological University, Delhi, India; Email: subratsarangi.dtu@gmail.com
Uddeshya Khanna, Department of Applied Mathematics, Delhi Technological University, Delhi, India; Email: uddeshya.khanna@gmail.com
Rohit Kumar, Department of Applied Mathematics, Delhi Technological University, Delhi, India; Email: rohitkumar@dtu.ac.in
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[1] R. Jain, M. Gupta, S. Taneja and D. Hemanth, "Deep learning based detection and analysis of COVID-19 on chest X-ray images", 2022. [Cross Ref]
-
[2] M. Che Azemin, R. Hassan, M. Mohd Tamrin and M. Md Ali, "COVID-19 Deep Learning Prediction Model Using Publicly Available Radiologist-Adjudicated Chest X-Ray Images as Training Data: Preliminary Findings", 2022.[Cross Ref]
-
[3] I. Apostolopoulos and T. Mpesiana, "Covid-19: automatic detection from X-ray images utilizing transfer learning with convolutional neural networks", 2022.[Cross Ref]
-
[4] A. Sharma, S. Rani and D. Gupta, "Artificial Intelligence-Based Classification of Chest X-Ray Images into COVID-19 and Other Infectious Diseases", 2022.[Cross Ref]
-
[5] Preprints.org, 2022. [Online]. Available: https://www.preprints.org/manuscript/202003.0300/v1/download. [Accessed: 30- Apr- 2022].[Cross Ref]
-
[6] T. Ozturk, M. Talo, E. Yildirim, U. Baloglu, O. Yildirim and U. Rajendra Acharya, "Automated detection of COVID-19 cases using deep neural networks with X-ray images", 2022.[Cross Ref]
-
[7] S. Minaee, R. Kafieh, M. Sonka, S. Yazdani and G. Jamalipour Soufi, "Deep-COVID: Predicting COVID-19 from chest X-ray images using deep transfer learning", 2022.[Cross Ref]
-
[8] P. Kedia, Anjum and R. Katarya, "CoVNet-19: A Deep Learning model for the detection and analysis of COVID-19 patients", 2022.[Cross Ref]
-
[9] S. Karakanis and G. Leontidis, "Lightweight deep learning models for detecting COVID-19 from chest X-ray images", 2022. [Cross Ref]
-
[10] A. Khan, J. Shah and M. Bhat, "CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images", 2022[Cross Ref]
-
[11] M. Chowdhury et al., "Can AI Help in Screening Viral and COVID-19 Pneumonia?” 2022. [Cross Ref]
-
[12] D. Kermany, K. Zhang and M. Goldbaum, "Labeled Optical Coherence Tomography (OCT) and Chest X-Ray Images for Classification", Mendeley Data, 2022. [Online]. Available: https://data.mendeley.com/datasets/rscbjbr9sj/2. [Accessed: 30- Apr-2022].[Cross Ref]
-
[13] J. Cohen, P. Morrison, L. Dao, K. Roth, T. Duong and M. Ghassemi, "COVID-19 Image Data Collection: Prospective Predictions Are the Future", arXiv.org, 2022. [Online]. Available: https://arxiv.org/abs/2006.11988. [Accessed: 30- Apr- 2022].[Cross Ref]
-
[14] "GitHub - agchung/Figure1-COVID-chestxray-dataset: Figure 1 COVID-19 Chest X-ray Dataset Initiative", GitHub, 2022. [Online]. Available: https://github.com/agchung/Figure1-COVID-chestxray-dataset. [Accessed: 30- Apr- 2022].[Cross Ref]
-
[15] "COVID-19 X rays", Kaggle.com, 2022. [Online]. Available: https://www.kaggle.com/andrewmvd/convid19-x-rays?select=X+rays [Accessed: 30- Apr- 2022].[Cross Ref]
-
[16] Mersha Nigus and H.L Shashirekha (2022), A Comparison of Machine Learning and Deep Learning Models for Predicting Household Food Security Status. IJEER 10(2), 308-311. DOI: 10.37391/IJEER.100241.[Cross Ref]
-
[17] Seong-Hyun Kim and Eui-Rim Jeong (2022), 1-Dimensional Convolutional Neural Network Based Blood Pressure Estimation with Photo plethysmography Signals and Semi-Classical Signal Analysis. IJEER 10(2), 214-217. DOI: 10.37391/IJEER.100228.[Cross Ref]
-
[18] Harendra Singh, Roop Singh, Parul Goel, Anil Singh and Naveen Sharma (2022), Automatic Framework for Vegetable Classification using Transfer-Learning. IJEER 10(2), 405-410. DOI: 10.37391/IJEER.100257.[Cross Ref]
Subrat Sarangi, Uddeshya Khanna and Rohit Kumar (2022), Ensemble Deep Convolution Neural Network for Sars-Cov-V2 Detection. IJEER 10(3), 481-486. DOI: 10.37391/IJEER.100313.