ULNet for the detection of coronavirus (COVID-19) from chest X-ray images

Abstract

Novel coronavirus disease 2019 (COVID-19) is an infectious disease that spreads very rapidly and threatens the health of billions of people worldwide. With the number of cases increasing rapidly, most countries are facing the problem of a shortage of testing kits and resources, and it is necessary to use other diagnostic methods as an alternative to these test kits. In this paper, we propose a convolutional neural network (CNN) model (ULNet) to detect COVID-19 using chest X-ray images. The proposed architecture is constructed by adding a new downsampling side, skip connections and fully connected layers on the basis of U-net. Because the shape of the network is similar to UL, it is named ULNet. This model is trained and tested on a publicly available Kaggle dataset (consisting of a combination of 219 COVID-19, 1314 normal and 1345 viral pneumonia chest X-ray images), including binary classification (COVID-19 vs. Normal) and multiclass classification (COVID-19 vs. Normal vs. Viral Pneumonia). The accuracy of the proposed model in the detection of COVID-19 in the binary-class and multiclass tasks is 99.53% and 95.35%, respectively. Based on these promising results, this method is expected to help doctors diagnose and detect COVID-19. Overall, our ULNet provides a quick method for identifying patients with COVID-19, which is conducive to the control of the COVID-19 pandemic.

TOPSIS aided ensemble of CNN models for screening COVID-19 in chest X-ray images

“…To test the effectiveness of the proposed TOPCONet model, we compare its performance with the performances of some state-of-the-art methods proposed by Khan et al 36 , Jain et al 57 , Hussain et al 37 , Ismael et al 58 , Das et al 25 , Goel et al 38 , and Paul et al 24 on Dataset-1 while for Dataset-2 we compare the methods proposed by Aslan et al 39 , Ouchicha et al 29 , Kedia et al 59 , Ahmad et al 60 , Chowdhury et al 56 , Sedik et al 18 , Wu et al 30 , Panetta et al 20 , Yang et al 61 , Paul et al 24 , Gour et al 33 , Gour et al 26 , Hasoon et al 27 , Bashar et al 34 , Senan et al 35 , Naeem et al 40 , Goyal et al 41 , Roy et al 28 and Senan et al 35 . To have a fair comparison with these methods include the experimental setups, especially the approach followed to prepare the samples of train and test sets, used by these methods.…”

“…To have a fair comparison with these methods include the experimental setups, especially the approach followed to prepare the samples of train and test sets, used by these methods. We found that two major approaches were used by these researchers: (1) partitioning the samples of the entire dataset into train and test sets 18 , 20 , 25 , 34 – 36 , 38 , 39 , 57 – 59 , 61 , as shown in Table 1 (we call this experimental setup as hold-out test set approach) and (2) standard 5-fold cross validation approach 26 , 27 , 29 , 30 , 33 , 37 , 40 , 41 , 56 , 60 as described in subsection “ Performance of TOPCONet model using 5-fold cross validation technique ”. However, the method designed by Paul et al 24 do not follow any of the mentioned experimental setups and hence we have developed the models at our end and evaluated following our experimental schemes.…”

“…The authors used two parallel CNN models and connected each of the layers of networks with inter-network and intra-network residual connections for feature transfer to encounter the vanishing gradient problem. Additionally, it is interesting to note that downsampling and skip connections have also been proposed in the literature in the past for COVID-19 detection 30 . Khuzani et al 31 employed multiple feature extraction schemes in which a novel pooling strategy was used to extract only the relevant features.…”

EVAE-Net: An Ensemble Variational Autoencoder Deep Learning Network for COVID-19 Classification Based on Chest X-ray Images

“…We compared our proposed model to various methods, as shown in Table 12 . The comparison shows the proposed model, EVAE-Net, outperformed the methods in [ 37 , 40 , 41 , 95 , 96 ] using the same dataset (COVID-19 Radiography Database) for COVID-19 classification and methods that used other modalities [ 17 , 38 , 49 , 97 ]. It was worth noting that most of these methods only focused on either three classes or four classes.…”

A fuzzy fine-tuned model for COVID-19 diagnosis

“…Another problem occurs when a lung disease, such as pneumonia, affects the lungs similarly to COVID-19. Many studies have not distinguished COVID-19 positive and positive pneumonia images [8] , [12] , [13] , [21] , [22] , [23] , [24] , [25] , [26] , [27] , [28] , [29] , [30] , [31] , [32] , [33] , [34] , [35] , [36] . When multi-class classification is performed, the severity of the problems related to the small dataset volume and the presence of noise is more apparent.…”