Novel deep transfer learning model for COVID-19 patient detection using X-ray chest images

Abstract

Around the world, more than 250 countries are affected by the COVID-19 pandemic, which is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This outbreak can be controlled only by the diagnosis of the COVID-19 infection in early stages. It is found that the radiographic images are ideal for the fastest diagnosis of COVID-19 infection. This paper proposes an ensemble model which detects the COVID-19 infection in the early stage with the use of chest X-ray images. The transfer learning enables to reuse the pretrained models. The ensemble learning integrates various transfer learning models, i.e., EfficientNet, GoogLeNet, and XceptionNet, to design the proposed model. These models can categorize patients as COVID-19 (+), pneumonia (+), tuberculosis (+), or healthy. The proposed model enhances the classifier's generalization ability for both binary and multiclass COVID-19 datasets. Two popular datasets are used to evaluate the performance of the proposed ensemble model. The comparative analysis validates that the proposed model outperforms the state-of-art models in terms of various performance metrics.

Artificial Intelligence of Things- (AIoT-) Based Patient Activity Tracking System for Remote Patient Monitoring

“…The sensor node runs a machine learning model to analyze the vitals during various activities. The sensor node detects the patient's body temperature, heart rate, and oxygen level when the patient is walking, sleeping, or exercising [ 42 , 43 ]. Analyzing vitals during various activities is critical because these vitals are different during various activities.…”

Emerging technologies for the management of COVID19: A review

“…Mujahid and Rajesh Rohilla” [64] “COVID_SCREENET: COVID-19 Screening in Chest Radiography Images Using Deep Transfer Stacking” [24] “R. Elakkiya, Pandi Vijayakumar and Marimuthu Karuppiah” [24] “Automatic detection of COVID-19 from chest CT scan and chest X-Rays images using deep learning, transfer learning and stacking” [46] “Ebenezer Jangam, Aaron Antonio Dias Barreto and Chandra Sekhara Rao Annavarapu” [44] “Rapid COVID‑19 diagnosis using ensemble deep transfer learning models from chest radiographic images” [42] “Neha Gianchandani, Aayush Jaiswal, Dilbag Singh, Vijay Kumar and Manjit Kaur” [41] “A Study of the Neutrosophic Set Significance on Deep Transfer Learning Models: an Experimental Case on a Limited COVID‑19 Chest X‑ray Dataset” [47] “Nour Eldeen M. Khalifa, FlorentinSmarandache, Gunasekaran Manogaran and Mohamed Loey” [45] “Novel deep transfer learning model for COVID‑19 patient detection using X‑ray chest images” [48] “N. Kumar, M. Gupta, D. Gupta and S. Tiwari” [46] “Res-CovNet: an internet of medical health things driven COVID-19 framework using transfer learning” [65] “Mangena VenuMadhavan, Aditya Khamparia, Deepak Gupta, Sagar Pande, Prayag Tiwari and M. Shamim Hossain” [62] “Transfer learning–based ensemble support vector machine model for automated COVID-19 detection using lung computerized tomography scan data” [66] “Mukul Sing,Shrey Bansal1, Sakshi Ahuja2, Rahul Kumar Dubey3,Bijaya Ketan Panigrahi2,Nilanjan Dey4” [63] Random Forest, Support Vector Machine (SVM) and KNN “Predicting the Probability of Covid-19 Recovered in South Asian Countries Based on Healthy Diet Pattern Using a Machine Learning Approach” [67] Md.…”

“…DL improves the medical diagnosis system (MDS) to achieve excellent outcomes, and implements a relevant real-time medical diagnosis system [47] . Ensemble learning incorporates several transfer learning models, including “EfficientNet, GoogLeNet, and XceptionNet.” Some models can classify patients as having “COVID-19 (+), pneumonia (+), tuberculosis (+), or being healthy” [48] . Because it does not require a huge annotated dataset for training, the transfer learning method is quicker and easier to deploy [41] .…”

Performance Evaluation of the Deep Learning Based Convolutional Neural Network Approach for the Recognition of Chest X-Ray Images

“…The number of filters in the next convolution layer increases to 128 which is followed by the max-pooling layer, and the last convolution layer has 256 filters ( 17 , 18 ). In the end, a flattening layer is introduced so that the obtained pooled feature maps can be fed to the array of the neural network ( 19 , 20 ). Two dense layers are introduced after the flattening layer which is followed by an output layer.…”