A Study of the Neutrosophic Set Significance on Deep Transfer Learning Models: an Experimental Case on a Limited COVID-19 Chest X-ray Dataset

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.…”

“… ✓ ✓ ✗ ✗ ML DNN [72] Precision -Recall -F1 score Immediate and rapid testing ✓ ✗ ✗ ✗ and [73] -Sensitivity -Specificity -Precision -F1-Score Diagonosis of medical images for covid detection. ✓ ✗ ✗ ✗ Clustering [47] Recall -Precision -Testing Accuracy -F1 score Classification between covid infected andpneumonic patients. classificationof ✓ ✗ ✗ ✗ DL Transfer Learning [65] -Accuracy -Recall -Precision -F1-score the COVID-19 affected patients from pneumoniaaffected patients.…”

“…DL is used in the diagnosis of medical x-rays and computed tomography. 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] .…”

AI-Empowered Computational Examination of Chest Imaging for COVID-19 Treatment: A Review

“…For ease of comparison, we summarize the reviewed methods for CXRs and CT scans in Table 1 and Table 2 Among the large volume of literature, transfer learning is one of the most common strategies in deep learning to combat data scarcity. It retrains a deep model on large-scale datasets and finetunes it on target COVID-19 image sets (Ahishali et al, 2021;Apostolopoulos and Mpesiana, 2020;Asnaoui and Chawki, 2020;Khan et al, 2020;Moutounet-Cartan, 2020;Narayan Das et al, 2020;Ozcan, 2020;Ozturk et al, 2020;Punn and Agarwal, 2021;Abbas et al, 2021b;Asif et al, 2021;Eldeen et al, 2021). These models include, but are not limited to, Inception, ResNet, VGG-16, NASNet, and AlexNet.…”

A Survey on Deep Transfer Learning and Edge Computing for Mitigating the COVID-19 Pandemic

“…• A 5-fold cross-validation has experimented and overall accuracy measured 95.3%0.02. N.E.M Khalifa et.al [112] An Experimental Case on a limited COVID-19 chest X-Ray dataset.…”