Deep Learning in Classification of Covid-19 Coronavirus, Pneumonia and Healthy Lungs on CXR and CT Images

Abstract

Purpose In this paper, the transfer learning method has been implemented to chest X-ray (CXR) and computed tomography (CT) bio-images of diverse kinds of lungs maladies, including CORONAVIRUS 2019 (COVID-19). COVID-19 identification is a difficult assignment that constantly demands a careful analysis of a patient’s clinical images, as COVID-19 is found to be very alike to pneumonic viral lung infection. In this paper, a transfer learning model to accelerate prediction processes and to assist medical professionals is proposed. Finally, the main purpose is to do an accurate classification between Covid-19, pneumonia and, healthy lungs using CXR and CT images. Methods Learning transfer gives the possibility to find out about this new illness COVID-19, using the knowledge we have about the pneumonia virus. This demonstrates the apprehensiveness achieved from a new architecture trained to detect virus-related pneumonia that must be transferred for COVID-19 detection. Transfer learning presents a considerable dissimilarity in results when compared to the result of traditional groupings. It is not necessary to create a separate model for the classification of COVID-19. This simplifies complicated issues by adopting the available model for COVID-19 determination. Automated diagnosis of COVID-19 using Haralick texture features is focused on segmented lung images and problematic lung patches. Lung patches are necessary for the augmentation of COVID-19 image data. Results The obtained outcomes are quite reliable for all distinctive processes as the proposed architecture can distinguish healthy lungs, pneumonia, COVID-19. Conclusions The results suggest that the implemented model is improved considering other existing models because the obtained classification accuracy is over the recently obtained results. It is a belief that the new architecture that is implemented in this study, delivers a petite step in building refined Coronavirus 2019 diagnosis architecture using CXR and CT bio-images.

Role of Artificial Intelligence in COVID-19 Detection

“…The usage of VGG19 [ 260 ] and VGG16 [ 264 ] have shown their significance in four-class classification, as noted in Table 5 . In [ 265 ], a combination of DenseNet103 with Haralick textural features and the ResNet101 model also showed promising performance. It is furthermore observed that for all modalities, only the VGG19 model is used for three-class categorization [ 273 ].…”

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.…”

“…For medical diagnosis, massive DL models incorporating “Convolutional Neural Networks (CNN)” were applied. Deep Methods such as “Stacked Auto-Encoder (SAE)”, “Deep Belief Network (DBN),” and “Deep Boltzmann Machine (DBM)”along with vector inputs are responsible for this [43] .The COVID19 disease damages the lungs of humans, which can be seen on a lung X-ray [44] . To forecast the Pneumonia case from chest X-rays, an effective CNN strategy was applied employing a convolutional neural network (CNN) [45] .…”

STCovidNet: Automatic Detection Model of Novel Coronavirus Pneumonia Based on Swin Transformer

“…This section extensively reviews the primary research methods used for the current COVID-19 cases detection. CNN is the most often used approach to solve the challenge of automated COVID-19 cases diagnosis [11], [12]. The deep learning frameworks in the previous studies are primarily based on the pretrained networks, including variants of Very Deep Convolutional Networks (VGGNet) [13], Deep Residual Neural Networks (ResNet) [14], Dense Convolutional Network (DenseNet) [15], Inception [16], Xception [17], MobileNet [18] and E cientNet [19].…”

“…DenseNet, developed by Huang et al [49], is designed to achieve more excellent anti-tting properties. DenseNet extends ResNet's shortcut connections by connecting all levels; each layer z i receives all preceding layers, z 0 , …, z i − 1 , as its new input to guarantee that the most inter-layer information is conveyed, as shown in (12):…”