Res-CovNet: an internet of medical health things driven COVID-19 framework using transfer learning

Abstract

Major countries are globally facing difficult situations due to this pandemic disease, COVID-19. There are high chances of getting false positives and false negatives identifying the COVID-19 symptoms through existing medical practices such as PCR (polymerase chain reaction) and RT-PCR (reverse transcription-polymerase chain reaction). It might lead to a community spread of the disease. The alternative of these tests can be CT (Computer Tomography) imaging or X-rays of the lungs to identify the patient with COVID-19 symptoms more accurately. Furthermore, by using feasible and usable technology to automate the identification of COVID-19, the facilities can be improved. This notion became the basic framework, Res-CovNet, of the implemented methodology, a hybrid methodology to bring different platforms into a single platform. This basic framework is incorporated into IoMT based framework, a web-based service to identify and classify various forms of pneumonia or COVID-19 utilizing chest X-ray images. For the front end, the.NET framework along with C# language was utilized, MongoDB was utilized for the storage aspect, Res-CovNet was utilized for the processing aspect. Deep learning combined with the notion forms a comprehensive implementation of the framework, Res-CovNet, to classify the COVID-19 affected patients from pneumonia-affected patients as both lung imaging looks similar to the naked eye. The implemented framework, Res-CovNet, developed with the technique, transfer learning in which ResNet-50 used as a pre-trained model and then extended with classification layers. The work implemented using the data of X-ray images collected from the various trustable sources that include cases such as normal, bacterial pneumonia, viral pneumonia, and COVID-19, with the overall size of the data is about 5856. The accuracy of the model implemented is about 98.4% in identifying COVID-19 against the normal cases. The accuracy of the model is about 96.2% in the case of identifying COVID-19 against all other cases, as mentioned.

Emerging technologies for the management of COVID19: A review

“…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. ShowrovHossenand Dip Karmoker [64] “PRELIMINARY DIAGNOSIS OF COVID-19 BASED ON COUGH SOUNDS USING MACHINELEARNING ALGORITHMS” [68] Arup Anupam Deptt.…”

“…of E&I NIT Silchar and Sudipta Chakraborty Dept.ofE&I NIT Silchar. [65] “Random-Forest-BaggingBroad Learning System with Applications for COVID-19 Pandemic” [69] Choujun Zhan, Yufan Zheng, Haijun ZhangandQuansiWen [61]

Figure 3 Emerging technologies for COVID 19

…”

“… ✓ ✗ ✗ ✗ 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. ✓ ✗ ✗ ✓ Ensemble [63] -AU-ROC -AU-PR Crowd management ✗ ✗ ✓ ✗ Heuristic optimization [74] -Distance -Route Cheaper and faster testing.…”

Monkeypox virus detection using pre-trained deep learning-based approaches

“…With the emerging growth of AI models in various domains such as chest x-ray images [30], fruit image analysis [25], and sentiment analysis [29,24], the AI models for medical image analysis have been proposed for various virusrelated disease detection [33,14]. For instance, Madhavan et al [14] developed a deep learning model (Res-COvNet) based on transfer learning approach for COVID-19 virus detection. They employed ResNet-50 [9] to extract the features from X-ray images and extended the network with a classification layer.…”

“…From existing research works on virus-related disease detection using DL methods, we observe that the majority of them have employed the transfer learning approach [8,14] using well-established pre-trained DL methods. Since there are not many works available on Monkeypox virus detection except the work by Ahsan et al [2].…”