1. Introduction
The coronavirus (COVID-19) was first discovered in a city called Wuhan in China’s Hubei Province. These types of cases were also reported earlier as severe acute respiratory syndrome (SARS-CoV) as well as Middle East respiratory syndrome (MERS) [1,2]. Initially, symptoms of COVID-19 were noticed in Wuhan, also known as the Hubei Province of China. This virus has been alarming for the health and comfort of the public. Also, it has widely affected the world economy, businesses, trades, and the stock market. This virus that affects the respiratory system of humans has been declared a pandemic globally. The virus has the same symptoms as that of viral pneumonia. These viruses cause mild to moderate respiratory diseases in humans. It is also noteworthy that it has a rising death rate if immediate treatment is not provided. In this virus, severe infection occurs in one or both pulmonary sacs, resulting in shortness of breath. Chest CT is utilized for analyzing as well as diagnosing pneumonia. Chest CT has also been remarkably beneficial in identifying the radiographic outlines of COVID-19 [3].
The World Health Organisation (WHO) [2] has suggested employing contact tracing and large-scale testing to handle the pandemic effectively. However, not all countries have the necessary infrastructure for mass testing. Reverse transcription polymerase chain reaction (RTPCR) is applied to distinguish viruses. Nasopharyngeal swabs are used for examining the presence of the ribonucleic acid (RNA) of SARS-CoV-2. This is a widespread method for the detection of this infection. Furthermore, the results of these tests may take 1–2 days if there are few cases, and it may take several days if there are many cases. During this period, there is a considerable probability of suspected patients who are waiting with slight or no symptoms transmitting the infection heavily.
COVID-19 has come to the notice of the government, healthcare workers, and researchers since it was declared a pandemic in March 2020. In this virus, whenever the patient has symptoms like fever, cough, runny nose, shortness of breath, etc., it indicates that the person might be infected with the coronavirus. Doctors refer those patients to lung X-ray imaging to detect the infection in the lungs. Most coronavirus-infected patients have abnormal lung X-rays, with the common observation that lower lobes of the lungs are affected by the peripheral ground glass opacity [4].
Vaccines and early disease diagnosis are the only cure available today to maintain a specific distance from the infected person. The new omicron variant of the coronavirus, which has caused worldwide outbreaks, is less superior than the earlier variants but more infectious. RT-PCR tests do detect the coronavirus. However, a limited number of test kits are available, and even then it is time-consuming. Thus, doctors prefer medical imaging technology like X-ray and chest computed tomography (CT) scans for detecting the prominent symptoms of the coronavirus in the images. Thus, machine learning and deep learning technologies are pivotal in analysing images and diagnosing diseases at an early stage. It helps reduce cases as the person can remain in isolation so that the infection does not spread to another person.
Expert radiologists and significant time are required to analyse lung X-rays, which is a challenge in this pandemic. Hence, a computer-assisted diagnosis (CAD) system is needed to assist doctors, especially radiologists, in analysing medical images for diagnosing COVID-19. The computer-assisted diagnosis (CAD) system is contingent on the image quality for promising results. Thus, there is a need for image-processing techniques for pre-processing, conversion, and enhancement, improving image quality. This process is the intermediate and necessary step for a computer-assisted diagnosis (CAD) system, which hinders the performance of automated learning. Therefore, deep learning (DL) techniques are superior for automatically extracting the features from the images without human involvement. Machine learning and deep learning are providing promising results for medical image analysis like breast cancer [5], lung cancer [6,7], brain tumours [8], classification of lung nodules [9,10], and many more.
This paper aims to use transfer-learning techniques by fine-tuning the hyper-parameters to detect coronavirus from chest X-ray images.
The contributions of our research are to explore state-of-the-art transfer learning techniques through literature analysis to propose a novel master–slave architecture. This is achieved by utilizing transfer-learning models like DenseNet201, SqueezeNet1_0, ResNet34, and VGG16 for COVID-19 detection using chest X-ray images. Also, hyperparameters, like the learning rate, are fine-tuned by finding the most optimized learning rate for increasing the model’s accuracy. Finally, the time and accuracy of the transfer-learning models are compared, and the proposed architecture is also compared using state-of-the-art techniques.
2. Literature Review
Machine-learning and deep-learning methods are widely utilized for computer vision [11,12], image processing [13], early medical diagnosis [14], and indexing [15]. The literature review considers deep-learning and transfer-learning techniques for COVID-19 detection using several medical image modalities like CT scans, X-rays, etc.
Researchers are working on finding new solutions to detect and diagnose the impact of COVID-19 on the lungs using medical imaging modalities. Many researchers are using transfer learning for the diagnosis of COVID-19. Apostolopoulos and Mpesiana [16] have done a comparative analysis of various transfer-learning models, like MobileNet v2, Xception, VGG19, Inception, and Inception ResNet v2. The dataset utilized in their experimentation includes 100 X-ray images with 50 images of patients with COVID-19 and 50 healthy humans. The highest accuracy achieved for 2-class classification is 98.75% and for 3-class classification is 93.48% using the VGG19 model. Apostolopoulos et al. [17] utilized the pre-trained model MobileNet to classify COVID-19 from 3905 X-ray images into six classes. Also, 455 X-ray images of COVID-19 patients are merged along with it. The pre-processing is performed to resize the images to 200 × 200 pixels, and data augmentation is performed to increase the dataset’s size. Their suggested model achieved 99.1% and 87.66% accuracy for binary and multiclass classification, respectively.
Similar to the above approaches, Loey et al. [18] suggested a generative adversarial network (GAN) for generating several images as there are limited standard chest X-ray image sequences. Correspondingly, transfer learning was utilized for detecting COVID-19. They used a dataset consisting of 307 images for four classes, i.e., bacterial pneumonia, viral pneumonia, COVID-19, and no findings. In their model, 90% of data was used along with validation. Pre-trained models like AlexNet, GoogleNet, and ResNet18 were used to classify the images into four classes. The accuracy achieved was 80.6% for four-class classification, 85.3% for three-class classification, and 100% for two-class classification.
Alternatively, Afshar et al. [19] proposed the following approach to detect COVID-19. The COVID-CAPS, a capsule-based network, was suggested to classify COVID-19 patients from healthy ones. The dataset used consists of X-ray images. Their approach achieves an accuracy of 95.7% along with considerably fewer parameters than the other trained models, while the pre-trained model achieves 98.3% accuracy. COVID-Net, a deep convolutional neural network (CNN), was proposed by Wang et al. [20] to diagnose COVID-19 from chest X-ray images. The authors also introduced the open-access dataset, which consists of 13,975 chest X-ray images. COVID-Net achieved 92.6% in a three-class classification problem.
To optimize the parameters of CNN models, various optimization methods are used. A similar approach is adopted by Goel et al. [21]. The authors proposed a model using optimized convolutional neural networks (OptCoNet) to classify COVID-19 patients among three classes, i.e., normal, pneumonia, and COVID-19. The grey wolf optimizer extracts optimized features and fine-tunes the trained model for higher accuracy. The proposed model consists of an optimized classification component with higher performance than other models, achieving 97.78% accuracy. Likewise, Sahlol et al. proposed a new approach FO-MPA for detecting COVID-19 [22]. The approach consisted of a pre-trained Inception model for feature extraction, while the swarm-based approach marine predators algorithm (MPA) was used for feature selection. The authors used two publicly available datasets for experimentation. The Kaggle datasets consist of X-ray images of patients infected with COVID-19. In their approach, 51K features were extracted from the Inception model. From that, 130 and 86 features were selected by the proposed model for dataset1 and dataset2, respectively. The authors proved that the accuracy is high when these methods are combined. The proposed approach achieved 98.7% and 98.2% for dataset1 and dataset2, respectively.
In contrast to the previous work, few authors focus on modifying the current techniques for better performance. Mor et al. [23] proposed an approach for the enhancement in detecting COVID-19 cases by utilizing a CycleGAN oversampling application. CycleGAN is used to reproduce suitable and applicable synthetic images that solve the problem of the lack of COVID-19 images and provide the solution to the low-quality problem in the images obtained from medical devices. The various experiments were performed using the data augmentation technique on a dataset consisting of chest X-rays and achieved 98.61% accuracy. Arora et al. [24] suggested a stochastic deep-learning approach for detecting COVID-19 infection using X-ray and CT scan images. Their approach accommodated deep representations over the gaussian before emphasizing the discrimination in feature maps. The experimentation was performed for three classes with publicly available datasets, i.e., normal, pneumonia, and COVID-19.
A class imbalance problem is solved for classifying COVID-19 in chest X-ray image sequences by Chamseddine et al. [25]. This approach utilized SMOTE and weighted loss. Feature selection technique and AdaBoost classifier are utilized for COVID-19 prediction by Soui et al. [26]. This approach used the patient’s symptoms for classification. Sentiment analysis of COVID-19 vaccines was conducted by Mansouri et al. [27]. This approach utilized TextBlob and BiLSTM for analysis. COVID-19 can be predicted using both chest X-ray and CT scan sequences. Chest X-ray is preferred as it takes less time and is cheap. This paper utilizes chest X-ray sequencing for COVID-19 detection.
Aside from the inspiring results produced by the various studies and analyses for COVID-19 detection using various medical modalities, a few complications need to be resolved for further studies and analysis. The fundamental problem for any deep neural network is the lack of balanced training data. Very few authors have worked on it to solve this issue by using augmentation techniques. Also, there is a lack of annotated data. Annotation requires expert knowledge, and it is also a time-consuming process. Thus, by solving these problems, the accuracy of the models can be increased.
3. Methodology
A widely used subset of machine learning known as deep learning imparts computers to learn through examples as humans naturally do. It is a prime foundation behind applications such as driverless cars, enabling cars to identify signs like go/halt/stop or to differentiate a person from a streetlight. It also helps to regulate speech in various consumer devices like speakers, mobile phones, TVs, tablets, etc. These techniques are receiving serious notice and providing accurate results that were not previously possible.
Deep-learning methods are computer models that learn to perform tasks straight from unstructured data like text, images, and sound, i.e., automatically extracting features. Deep learning models can perform better than other algorithms providing more accurate results. It also exceeds human-level classification in some cases. Here, a massive set of labelled data and architectures of the many-layered neural network are utilized for training the models.
The proposed master–slave architecture is shown in Figure 1. The architecture is divided into two modules named master and slave. The master module is responsible for pre-processing the dataset and training the transfer-learning models. The trained model’s layers are made static or frozen. Although the slave module will learn from the master module by utilizing the freeze layers, only the last few layers will be trained again to optimize the parameters. Thus, the slave module is responsible for optimization. A detailed description of the steps performed in the proposed approach is described in Algorithm 1.
| Algorithm 1: Proposed Master-Slave Algorithm for detecting COVID-19. |
| Assume i = chest X-ray image, p = pre-processing |
Below is a detailed description of the master and slave modules and sub-modules.
3.1. Dataset
In our research, the dataset utilized for the study consists of X-ray images prepared by integrating images from two different data sources that were used to detect COVID-19 infection. The first dataset comprises COVID-19 X-ray images developed by Cohen et al. [28], including images retrieved from several open-access resources. Also, this source regularly updates images shared by several researchers from different regions. This dataset currently contains 127 X-ray images diagnosed with COVID-19. This dataset consists of 43 female and 82 male patients diagnosed as COVID-19 positive. The second dataset incorporates chest X-rays by Wang et al. [29] that contains normal and pneumonia images. These data sources were integrated and utilized by Ozturk et al. [30,31]. They utilized 500 images of healthy lungs and 500 with pneumonia on frontal chest X-ray images. The images were selected randomly. This was done to prevent the challenge of unbalanced data. In our approach, 100 images from each group include COVID-19, no findings, and pneumonia. A few images of the dataset are shown in Figure 2.
3.2. Data Pre-Processing
The images are pre-processed to resize them to 256 × 256 to make all the images the same shape and normalized. In this approach, the utilized dataset is separated into training data and validation data. Overall, 80% of the images are used for training, while 20% are for validation.
3.3. Transfer Learning
Deep learning is extensively and effectively used in various domains where there is a need to extract hidden patterns from historical data to predict future results [32]. ML techniques are typically categorized by training and testing datasets containing features and the target variable. There is a high fluctuation in results based on the distribution of the datasets [33]. Hence, there is a requirement for a vast dataset. In many cases, it is impossible to find sizeable training data that matches the feature space giving high accuracy. Additionally, it is challenging to make accurate predictions regarding the data distribution characteristics of test data. As a result, there is high demand for sophisticated learners that can learn more efficiently from small data and achieve high performance. It is possible by transferring the domain knowledge of one task to another related task. It is a fundamental principle for applying transfer learning to various fields.
Transfer learning is based on real-world scenarios. For example, if someone knows how to ride a bike, they can ride a car using the same knowledge. Humans transfer knowledge gained from one domain to another to learn things fast and more efficiently. Similarly, transfer learning uses the previously learned models on some datasets to map the gained knowledge using new data. However, there is a difference between typical ML and transfer learning. In typical ML, algorithms are used to train the model from the start without retaining the knowledge gained from previous training. In contrast, transfer learning retains the previously gained knowledge to train with the new domain or data, as shown in Figure 3.
The various standard pre-trained models for classifying medical images are discussed below.
Consider an image i0 that is given as an input to the transfer-learning model, i.e., in the convolutional network. Suppose there are L layers that represent information that is non-linear Tℓ (·), where ℓ is the index of the layer Tℓ(·). Tℓ (·) is a compound function that consists of various types of procedures like convolution (Conv), dropout, batch normalization (BN) [34], activation functions like rectified linear units (ReLU) [35], various types of pooling [36] like max pooling, min pooling, average pooling, etc. The output of the ℓth layer is iℓ.
3.3.1. ResNets
The typical convolutional neural networks, for each ℓth layer, connect the input with the output layer (ℓ+1)th layer [37], thus making the transition from iℓ to Tℓ (iℓ−1). ResNets [38] are famous for solving the degradation problem by introducing the skip connections that sidestep the non-linear transformations as given in Equation (1).
Iℓ = Tℓ (iℓ−1) + iℓ−1.(1)
The advantages of using ResNets are that the gradient is not diminished and can be passed to later layers through the identity function. However, the output of Tl is added with an identity function that can delay the information flow in the network.
3.3.2. DenseNet
Direct connections are also introduced from the layers to all the succeeding layers in ResNets to combat the issue of information delay [39]. These connections help to improve the information flow in the network. As a result, the lth layer’s feature maps are taken into account by all of the layers (i0, i1, i2…., iℓ−1) that came before it as input:
il = Tl ([i0, i1,…, iℓ−1])(2)
where [i0, i1, i2…., iℓ−1] depicts the combination of the generated feature maps through layers 0,1,2,3—ℓ−1. As this network is densely connected, it is known as DenseNet.3.3.3. DarkNet
DarkNet-19 is the standard model that is used to detect objects. It is designed for classifying objects in a real-time environment which is widely known as “You Only Look Once” (YOLO) [40]. This architecture is utilized for medical imaging because of its successful architecture. Hence, inspired by DarkNet, DarkCovidNet [30] is proposed, which is an optimized model with few layers and filters in contrast to the original DarkNet.
3.3.4. VGG16
VGGNet [41] is designed by Oxford University and is trained on the dataset of ImageNET. The architecture of VGG16 uses 16 weight layers containing 13 convolutional layers having a 3 × 3 filter size and three fully connected layers with a 3 × 3 filter size. AlexNet, as well as VGG16, have the same configurations. The convolutional layers are placed in five groups. In contrast, the max-pooling layer is followed after each group. The padding and stride are kept to 1 pixel for all the convolutional layers. The disadvantage of using the VGG16 is its significant architecture consisting of 160M parameters.
3.3.5. SqueezeNet
SqueezeNet [42] is trained on the ImageNet dataset achieving AlexNet-level accuracy but with smaller network architecture. The larger architectures of convolutional deep neural networks create a bottleneck regarding resource utilization and time consumption. The advantages of using the smaller networks with similarly accurate results are distributed learning and less communication across the servers, reducing the training time. Fewer parameters in the network result in using the lesser band to export the model from the cloud for transfer learning, and smaller networks are feasible for being deployed with fewer hardware requirements like limited memory, etc. Consequently, SqueezeNet was proposed in 2016 with a small CNN architecture having 50× fewer parameters to offer all these advantages. Furthermore, while compressing the model by using compression techniques, SqueezeNet is compressed to less than 0.5 MB.
3.4. Performance Measure
The proposed method is evaluated using standard parameters like accuracy and cross-entropy loss.
3.4.1. Accuracy
Accuracy is measured as the ratio of the total count of correctly classified instances and the total count of instances in the dataset.
(3)
where Tp = true positive and Tn = true negative.3.4.2. Cross-Entropy Loss or Logloss
Binary cross entropy is the negative average of the logarithm of correctly predicted probabilities. It compares each predicted probability to the actual label output for binary classification. The score is based on the difference from the actual expected value to penalize the probabilities [43].
(4)
where N depicts the total instances, L represents class labels, yij represents the actual value of the ith instance in the jth class, and pij represents the predicted value of the ith instance in the jth class.3.5. Training and Classification
The proposed architecture uses pre-trained models like DarkNet, VGG16, ResNet34, SqueezeNet1_0, and DenseNet201. The initial layers of the model are frozen while the learning rate is fine-tuned to optimize the model for higher accuracy. The last few layers are retrained to obtain the optimized model. The advantage of this approach is it does not increase the complexity of the standard DenseNet201 model.
3.6. Test and Evaluation
The proposed model is evaluated and tested using standard performance metrics like accuracy and cross-entropy loss.
4. Results and Discussion
This section showcases experiments performed for multiclass classification using the proposed model DenseNet201 to differentiate COVID-19 X-ray images from those of normal and pneumonia cases. The proposed approach is evaluated using the other four models, i.e., DarkNet, ResNet34, SqueezeNet1_0, and VGG16. It is performed using python, especially utilizing fastai, a deep learning library. The various parameters for all four models are given in Table 1. All the models are trained for ten epochs.
Table 2 represents the models’ training loss, validation loss, and accuracy after 10 epochs. From the table, DenseNet201 outperforms other models in classifying COVID-19 images, achieving 85.00% accuracy.
Figure 4, Figure 5, Figure 6, Figure 7 and Figure 8 depict the assessment of training as well as validation losses for DarkNet, ResNet34, VGG16, SqueezeNet1_0, and DenseNet201, respectively.
From the above Figure 4, Figure 5, Figure 6, Figure 7 and Figure 8, it is clear that, initially, the training and validation losses are high, then, as the epochs increase, they eventually decrease. Mostly, the training and validation losses stabilize for all the models trained after 65 batches. After 10 epochs, i.e., 70 batches, there is a minor change in training and validation losses for every model. It also depicts that the losses of the DenseNet201 model are less compared to the other models, proving it to be better than the other four models.
Figure 9, Figure 10, Figure 11 and Figure 12 illustrate the comparison of accuracy, training loss, validation loss, and time across the 10 epochs, respectively.
Figure 9, Figure 10, Figure 11 and Figure 12 depict that the accuracy of all the models has increased considerably after the eighth epoch and stabilizes at the ninth epoch. Figure 9, Figure 10 and Figure 11 also showcase that DenseNet201 outperforms other models achieving an accuracy of 83%, while DenseNet201 and SqueezeNet1_0 have minimum losses in contrast to the other four models. Moreover, the fastest models among the five are DarkNet and SqueezeNet, as depicted in Figure 11.
The transfer-learning models are fine-tuned to find the best learning rate. Figure 13, Figure 14, Figure 15 and Figure 16 depict the learning rate concerning the loss for ResNet34, VGG16, SqueezeNet1_0, and DenseNet201, respectively. The red dot indicates the best learning rate for the respective model that can give the highest accuracy. Thus, for all the methods, the learning rate is selected to be nearer to the red dot, as shown in Figure 13, Figure 14, Figure 15 and Figure 16.
The best learning rate and the accuracy obtained after optimizing the learning rate parameter are showcased in Table 3. It also compares the accuracy before and after fine-tuning the hyperparameter learning rate.
Thus, Table 3 shows that the accuracy of the models ResNet34, VGG16, and SqueezeNet is increased while the accuracy of the DenseNet201 model is the same. Nevertheless, in terms of accuracy, it performs better than any of the other four models.
The results of the DenseNet201 and SqueezeNet1_0 models after fine-tuning are showcased in Figure 17 and Figure 18. In Figure 17 and Figure 18, the first represents the ground truth value, and the second represents the predicted value for the given image below.
The research proposes a novel master–slave methodology that utilizes the transfer-learning approach for detecting COVID-19 by analyzing chest X-ray images. Among the models used, the DenseNet201 model outperforms, giving an accuracy of 83.33%, while the fastest model is SqueezeNet1_0, achieving an accuracy of 80% after fine-tuning the learning rate. DenseNet201 performs well on the small dataset, extracting low and high-complexity-level features. Hence, the time required by the model is high. In contrast to the DenseNet201 model, SqueezeNet1_0 is 20 times faster, giving comparable accuracy as it has lightweight architecture with significantly fewer trainable parameters. The training process for deep-learning models is time-consuming. Therefore, high-performance graphical processing units (GPUs) can achieve faster results.
5. Conclusions and Future Work
The new coronavirus disease 2019 (COVID-19) has caused a worldwide pandemic. There is an increased demand for COVID-19 testing and diagnosis due to the high number of cases. Cases of this virus are increasing daily, putting a significant strain on our social, emotional, physical, and mental health. Moreover, early detection of COVID-19 cases is a crucial and beneficial step in the virus’ prevention. One of the primary reasons for the rapid spread of COVID-19 cases is the traditional time-consuming testing process. A chest X-ray or other imaging tool could be used instead to expedite the testing process and stop the spread of the virus. As a result, this study aims to create a computer-assisted diagnosis (CAD) system to classify samples of COVID-19 from a chest X-ray dataset that includes pneumonia, COVID-19, and healthy cases. The study focuses on analyzing X-ray images for early COVID-19 detection. With an accuracy of 83.33%, the proposed master–slave approach with DenseNet201 outperforms other models. The research can be expanded in the future to improve model accuracy by using data augmentation techniques to increase the dataset size. Furthermore, GPUs can be used to train models quickly.
Conceptualization, N.A.; methodology, N.A.; software, N.A.; resources, N.A.; supervision, A.A.; project administration, A.A. All authors have read and agreed to the published version of the manuscript.
This article does not contain any studies with human participants performed by authors.
Not applicable.
Data sharing does not apply to this article as no datasets were generated during the current study.
The authors declare no conflict of interest.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Parameters used for the proposed methodology.
| Model | Depth | Number of Parameters | Optimizer |
|---|---|---|---|
| DarkNet | 56 | 1,167,586 | Adam |
| ResNet34 | 34 | 21,814,083 | Adam |
| VGG16 | 16 | 15,252,547 | Adam |
| SqueezeNet1_0 | 14 | 1,264,835 | Adam |
| DenseNet201 | 201 | 20,069,763 | Adam |
Results of all the models after 10 epochs.
| Model | Train Loss | Valid loss | Accuracy |
|---|---|---|---|
| DarkNet | 0.638718 | 0.515339 | 0.800000 |
| ResNet34 | 0.233139 | 0.823872 | 0.750000 |
| VGG16 | 0.171060 | 0.792268 | 0.766667 |
| SqueezeNet1_0 | 0.168784 | 0.516652 | 0.783333 |
| DenseNet201 | 0.135499 | 0.601770 | 0.833333 |
Fine-tuned learning rate and accuracy.
| Model | Initial Learning Rate | Learning Rate after Fine-Tuning | Accuracy before Fine-Tuning | Accuracy after Fine-Tuning |
|---|---|---|---|---|
| DarkNet [ |
3e−3 | - | 0.800000 | - |
| ResNet34 [ |
Default | 1e−06, 1e−07 | 0.750000 | 0.783333 |
| VGG16 [ |
Default | 1e−07, 1e−06 | 0.766667 | 0.800000 |
| SqueezeNet1_0 | Default | 1e−06, 1e−07 | 0.783333 | 0.800000 |
| DenseNet201 | Default | 1.58e−06 | 0.833333 | 0.833333 |
References
1. Cheng, V.C.C.; Lau, S.K.P.; Woo, P.C.Y.; Yuen, K.-Y. Severe acute respiratory syndrome coronavirus as an agent of emerging and reemerging infection. Clin. Microbiol. Rev.; 2007; 20, pp. 660-694. [DOI: https://dx.doi.org/10.1128/CMR.00023-07] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/17934078]
2. Chan, J.F.; Lau, S.K.; To, K.K.; Cheng, V.C.; Woo, P.C.; Yuen, K.Y. Middle East respiratory syndrome coronavirus: Another zoonotic betacoronavirus causing SARS-like disease. Clin. Microbiol. Rev.; 2015; 28, pp. 465-522. [DOI: https://dx.doi.org/10.1128/CMR.00102-14] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/25810418]
3. Aishwarya, T.; Kumar, V.R. Machine Learning and Deep Learning Approaches to Analyze and Detect COVID-19: A Review. SN Comput. Sci.; 2021; 2, 226. [DOI: https://dx.doi.org/10.1007/s42979-021-00605-9] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/33899005]
4. Rousan, L.A.; Elobeid, E.; Karrar, M.; Khader, Y. Chest X-ray findings and temporal lung changes in patients with COVID-19 pneumonia. BMC Pulm. Med.; 2020; 20, 245. [DOI: https://dx.doi.org/10.1186/s12890-020-01286-5]
5. Wang, D.; Khosla, A.; Gargeya, R.; Irshad, H.; Beck, A.H. Deep learning for identifying metastatic breast cancer. arXiv; 2016; arXiv: 1606.05718
6. Suk, H.I.; Shen, D. Deep learning-based feature representation for AD/MCI classification. Proceedings of the International Conference on Medical Image Computing and Computer-Assisted Intervention; Nagoya, Japan, 22–26 September 2013; Springer: Berlin/Heidelberg, Germany, 2013; pp. 583-590.
7. Liu, S.; Liu, S.; Cai, W.; Pujol, S.; Kikinis, R.; Feng, D. Early diagnosis of Alzheimer’s disease with deep learning. Proceedings of the 11th International Symposium on Biomedical Imaging (ISBI); Beijing, China, 29 April–2 May 2014; IEEE: Piscataway, NJ, USA, 2014; pp. 1015-1018.
8. Israni, P.; Patel, M. Medical Image Analysis (MedIA) using deep learning. Int. J. Innov. Technol. Explor. Eng. (IJITEE); 2020; 9, pp. 21-25.
9. Cheng, J.-Z.; Ni, D.; Chou, Y.-H.; Qin, J.; Tiu, C.-M.; Chang, Y.-C.; Huang, C.-S.; Shen, D.; Chen, C.-M. Computer-aided diagnosis with deep learning architecture: Applications to breast lesions in US images and pulmonary nodules in CT scans. Sci. Rep.; 2016; 6, 24454. [DOI: https://dx.doi.org/10.1038/srep24454]
10. Chen, J.; Chen, J.; Ding, H.-Y.; Pan, Q.-S.; Hong, W.-D.; Xu, G.; Yu, F.-Y.; Wang, Y.-M. Use of an artificial neural network to construct a model of predicting deep fungal infection in lung cancer patients. Asian Pac. J. Cancer Prev.; 2015; 16, pp. 5095-5099. [DOI: https://dx.doi.org/10.7314/APJCP.2015.16.12.5095]
11. Israni, D.; Mewada, H. Identity Retention of Multiple Objects under Extreme Occlusion Scenarios using Feature Descriptors. J. Commun. Softw. Syst.; 2018; 14, pp. 290-301. [DOI: https://dx.doi.org/10.24138/jcomss.v14i4.541]
12. Israni, D.; Mewada, H. Feature descriptor based identity retention and tracking of players under intense occlusion in soccer videos. Int. J. Intell. Eng. Syst.; 2018; 11, pp. 31-41. [DOI: https://dx.doi.org/10.22266/ijies2018.0831.04]
13. Israni, D.; Bhatt, M. Embedding Color Video Watermark in Image using Orthogonal and Bi-orthogonal Wavelet Transform. Proceedings of the International Conference on Advances in Computer Science and Application; Lucknow, India, 21–22 June 2013; pp. 624-631.
14. Israni, P. Breast cancer diagnosis (BCD) model using machine learning. Int. J. Innov. Technol. Explor. Eng.; 2019; 8, pp. 4456-4463. [DOI: https://dx.doi.org/10.35940/ijitee.J9973.0881019]
15. Israni, P.; Israni, D. An indexing technique for fuzzy object oriented database using R tree index. Proceedings of the International Conference on Soft Computing and Its Engineering Applications (icSoftComp); Changa, India, 1–2 December 2017; IEEE: Piscataway, NJ, USA, 2018; pp. 1-5.
16. IApostolopoulos, I.D.; Mpesiana, T.A. COVID-19: Automatic detection from X-ray images utilizing transfer learning with convolutional neural networks. Phys. Eng. Sci. Med.; 2020; 43, pp. 635-640. [DOI: https://dx.doi.org/10.1007/s13246-020-00865-4] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/32524445]
17. Apostolopoulos, I.D.; Aznaouridis, S.I.; Tzani, M.A. Extracting possibly representativeCOVID-19 Biomarkers from X-Ray images with Deep Learning approach and image data related to Pulmonary Diseases. J. Med. Biol. Eng.; 2020; 40, pp. 462-469. [DOI: https://dx.doi.org/10.1007/s40846-020-00529-4] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/32412551]
18. Loey, M.; Smarandache, F.; Khalifa, N.E.M. Within the Lack of COVID-19 Benchmark Dataset: A Novel GAN with Deep Transfer Learning for Corona-virus Detection in Chest X-ray Images. Symmetry; 2020; 12, 651. [DOI: https://dx.doi.org/10.3390/sym12040651]
19. Afshar, P.; Heidarian, S.; Naderkhani, F.; Oikonomou, A.; Plataniotis, K.N.; Mohammadi, A. Covid-caps: A capsule network-based framework for identification of covid-19 cases from x-ray images. Pattern Recognit. Lett.; 2020; 138, pp. 638-643. [DOI: https://dx.doi.org/10.1016/j.patrec.2020.09.010] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/32958971]
20. Wang, L.; Lin, Z.Q.; Wong, A. COVID-Net: A tailored deep convolutional neural network design for detection of COVID-19 cases from chest radiography images. Sci. Rep.; 2020; 10, 19549. [DOI: https://dx.doi.org/10.1038/s41598-020-76550-z]
21. Goel, T.; Murugan, R.; Mirjalili, S.; Chakrabartty, D.K. OptCoNet: An optimized con-volutional neural network for an automatic diagnosis of COVID-19. Appl. Intell.; 2020; 51, pp. 1351-1366. [DOI: https://dx.doi.org/10.1007/s10489-020-01904-z]
22. Sahlol, A.T.; Yousri, D.; Ewees, A.A.; Al-Qaness, M.A.A.; Damasevicius, R.; Elaziz, M.A. COVID-19 image classification using deep features and fractional-ordermarine predators algorithm. Sci. Rep.; 2020; 10, 15364. [DOI: https://dx.doi.org/10.1038/s41598-020-71294-2]
23. Morís, D.I.; Ramos, J.J.D.M.; Buján, J.N.; Hortas, M.O. Data augmentation approaches using cycle-consistent adversarial networks for improving COVID-19 screening in portable chest X-ray images. Expert Syst. Appl.; 2021; 185, 115681. [DOI: https://dx.doi.org/10.1016/j.eswa.2021.115681]
24. Arora, R.; Bansal, V.; Buckchash, H.; Kumar, R.; Sahayasheela, V.J.; Narayanan, N.; Pandian, G.N.; Raman, B. AI-based diagnosis of COVID-19 patients using X-ray scans with stochastic ensemble of CNNs. Phys. Eng. Sci. Med.; 2021; 44, pp. 1257-1271. [DOI: https://dx.doi.org/10.1007/s13246-021-01060-9]
25. Chamseddine, E.; Mansouri, N.; Soui, M.; Abed, M. Handling class imbalance in COVID-19 chest X-ray images classification: Using SMOTE and weighted loss. Appl. Soft Comput.; 2022; 129, 109588. [DOI: https://dx.doi.org/10.1016/j.asoc.2022.109588] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/36061418]
26. Soui, M.; Mansouri, N.; Alhamad, R.; Kessentini, M.; Ghedira, K. NSGA-II as feature selection technique and AdaBoost classifier for COVID-19 prediction using patient’s symptoms. Non-Linear Dyn.; 2021; 106, pp. 1453-1475. [DOI: https://dx.doi.org/10.1007/s11071-021-06504-1] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/34025034]
27. Mansouri, N.; Soui, M.; Alhassan, I.; Abed, M. TextBlob and BiLSTM for Sentiment analysis toward COVID-19 vaccines. Proceedings of the 7th International Conference on Data Science and Machine Learning Applications (CDMA); Salahuddin, Riyadh, 1–3 March 2022; pp. 73-78. [DOI: https://dx.doi.org/10.1109/CDMA54072.2022.00017]
28. Cohen, J.P. COVID-19 Image Data Collection. 2020; Available online: https://github.com/ieee8023/COVID-chestxray-dataset (accessed on 10 October 2022).
29. Wang, X.; Peng, Y.; Lu, L.; Lu, Z.; Bagheri, M.; Summers, R.M. Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; Honolulu, HI, USA, 21–26 July 2017; pp. 2097-2106. [DOI: https://dx.doi.org/10.1109/CVPR.2017.369]
30. Ozturk, T.; Talo, M.; Yildirim, E.A.; Baloglu, U.B.; Yildirim, O.; Acharya, U.R. Automated detection of COVID-19 cases using deep neural networks with X-ray images. Comput. Biol. Med.; 2020; 121, 103792. [DOI: https://dx.doi.org/10.1016/j.compbiomed.2020.103792] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/32568675]
31. Talo, M. Covid-19. 2021; Available online: https://github.com/muhammedtalo/COVID-19 (accessed on 10 October 2022).
32. Witten, I.H.; Frank, E.; Hall, M.A.; Pal, C.J. Data Mining, Practical Machine Learning Tools and Techniques; 3rd ed. Morgan Kaufmann Publishers: San Francisco, CA, USA, 2011; [DOI: https://dx.doi.org/10.1016/C2009-0-19715-5]
33. Pan, S.J.; Yang, Q. A survey on transfer learning. IEEE Trans. Knowl. Data Eng.; 2009; 22, pp. 1345-1359. [DOI: https://dx.doi.org/10.1109/TKDE.2009.191]
34. Ioffe, S.; Szegedy, C. Batch normalization: Accelerating deep network training by reducing internal covariate shift. Proceedings of the International Conference on Machine Learning; Lille, France, 6–11 July 2015; Volume 37, pp. 448-456. [DOI: https://dx.doi.org/10.5555/3045118.3045167]
35. Glorot, X.; Bordes, A.; Bengio, Y. Deep sparse rectifier neural networks. Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, JMLR Workshop and Conference Proceedings; Fort Lauderdale, FL, USA, 11–13 April 2011; pp. 315-323.
36. Lecun, Y.; Bottou, L.; Bengio, Y.; Haffner, P. Gradient based learning applied to document recognition. Proc. IEEE; 1998; 86, pp. 2278-2324. [DOI: https://dx.doi.org/10.1109/5.726791]
37. Krizhevsky, A.; Sutskever, I.; Hinton, G.E. ImageNet classification with deep convolutional neural networks. Adv. Neural Inf. Process. Syst.; 2012; 25, pp. 1-9. [DOI: https://dx.doi.org/10.1145/3065386]
38. He, K.; Zhang, X.; Ren, S.; Sun, J. Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR); Las Vegas, NV, USA, 27–30 June 2016; pp. 770-778. [DOI: https://dx.doi.org/10.1109/CVPR.2016.90]
39. Huang, G.; Liu, Z.; Van Der Maaten, L.; Weinberger, K.Q. Densely connected convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; Honolulu, HI, USA, 21–26 July 2017; pp. 4700-4708. [DOI: https://dx.doi.org/10.1109/CVPR.2017.243]
40. Redmon, J.; Farhadi, A. Yolo9000: Better, Faster, Stronger. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition; Honolulu, HI, USA, 21–26 July 2017; pp. 7263-7271. [DOI: https://dx.doi.org/10.1109/CVPR.2017.690]
41. Simonyan, K.; Zisserman, A. Very deep convolutional networks for large-scale image recognition. Proceedings of the 3rd International Conference on Learning Representations; ICLR, San Diego, CA, USA, 7–9 May 2015; pp. 1-14.
42. Landola, F.N.; Han, S.; Moskewicz, M.W.; Ashraf, K.; Dally, W.J.; Keutzer, K. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5 MB model size. arXiv; 2016; arXiv: 1602.07360
43. Saxsena, S. Binary Cross Entropy/Log Loss for Binary Classification. 2021; Available online: https://www.analyticsvidhya.com/blog/2021/03/binary-cross-entropy-log-loss-for-binary-classification/ (accessed on 2 July 2021).
44. Pal, M.; Tiwari, Y.; Reddy, T.V.; Parisineni, S.R.A.; Panigrahi, P.K. An integrative method for COVID-19 patients classification from chest X-ray using deep learning network with image visibility graph as feature extractor. medRxiv; 2021; pp. 1-22. [DOI: https://dx.doi.org/10.1101/2021.11.17.21266472]
45. Panthakkan, A.; Anzar, S.; Al Mansoori, S.; Al Ahmad, H. Accurate prediction of covid-19 (+) using ai deep vgg16 model. Proceedings of the 3rd International Conference on Signal Processing and Information Security (ICSPIS); Dubai, United Arab Emirates, 25–26 November 2020; pp. 1-4. [DOI: https://dx.doi.org/10.1109/ICSPIS51252.2020.9340145]
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