A Novel CovidDetNet Deep Learning Model for Effective COVID-19 Infection Detection Using Chest Radiograph Images

Ullah, Naeem; Khan, Javed Ali; Almakdi, Sultan; Khan, Mohammad Sohail; Alshehri, Mohammed; Alboaneen, Dabiah; Raza, Asaf

doi:10.3390/app12126269

Cited by 31 publications

(24 citation statements)

References 58 publications

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“…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.…”

Section: Results and Analysismentioning

confidence: 99%

“…The proposed model successfully differentiated between COVID-19 pneumonia and community-acquired pneumonia (CAP) with sensitivity and specificity rates of 90% and 96%, respectively. A novel deep learning model, CovidDetNet, for detecting COVID-19 infections using chest radiograph images was proposed by Ullah et al [ 37 ]. The proposed model comprises nine convolutional layers, one fully-connected layer, two activation functions (ReLU and Leaky ReLU), and two normalization operations (batch normalization and cross-channel normalization) and achieved an accuracy of 98.40%.…”

Section: Related Workmentioning

confidence: 99%

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

et al. 2022

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The COVID-19 pandemic has had a significant impact on many lives and the economies of many countries since late December 2019. Early detection with high accuracy is essential to help break the chain of transmission. Several radiological methodologies, such as CT scan and chest X-ray, have been employed in diagnosing and monitoring COVID-19 disease. Still, these methodologies are time-consuming and require trial and error. Machine learning techniques are currently being applied by several studies to deal with COVID-19. This study exploits the latent embeddings of variational autoencoders combined with ensemble techniques to propose three effective EVAE-Net models to detect COVID-19 disease. Two encoders are trained on chest X-ray images to generate two feature maps. The feature maps are concatenated and passed to either a combined or individual reparameterization phase to generate latent embeddings by sampling from a distribution. The latent embeddings are concatenated and passed to a classification head for classification. The COVID-19 Radiography Dataset from Kaggle is the source of chest X-ray images. The performances of the three models are evaluated. The proposed model shows satisfactory performance, with the best model achieving 99.19% and 98.66% accuracy on four classes and three classes, respectively.

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Section: Results and Analysismentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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

et al. 2022

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“…and classify the types of brain tumors like benign or malignant. Additionally, to further generalize the proposed approach in detecting other important medical diseases [ 44 ] together with the brain MRI, we aim to identify and capture the performance of the TumorResNet model by training and validating it on the identification of Covid-19 [ 45 ] from chest radiograph images [ 34 ], pest detection [ 46 ], other popular brain tumor types [ 47 ], predicting heart diseases [ 48 , 49 ], and mask detecting & removal [ 50 , 51 ] to generalize it further.…”

Section: Discussionmentioning

confidence: 99%

“…As the dying ReLU issue progressively renders a significant percentage of the network inactive, it is undesired [ 33 ]. To tackle the dying ReLU problem, we employed the LreLU [ 34 ] activation function in the first full convolution layer. LreLU uses a tiny positive value, such as 0.1 rather than 0, to activate all neurons for most training instances.…”

Section: Methodsmentioning

confidence: 99%

A Robust End-to-End Deep Learning-Based Approach for Effective and Reliable BTD Using MR Images

Ullah

Khan

et al. 2022

Sensors

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Detection of a brain tumor in the early stages is critical for clinical practice and survival rate. Brain tumors arise in multiple shapes, sizes, and features with various treatment options. Tumor detection manually is challenging, time-consuming, and prone to error. Magnetic resonance imaging (MRI) scans are mostly used for tumor detection due to their non-invasive properties and also avoid painful biopsy. MRI scanning of one patient’s brain generates many 3D images from multiple directions, making the manual detection of tumors very difficult, error-prone, and time-consuming. Therefore, there is a considerable need for autonomous diagnostics tools to detect brain tumors accurately. In this research, we have presented a novel TumorResnet deep learning (DL) model for brain detection, i.e., binary classification. The TumorResNet model employs 20 convolution layers with a leaky ReLU (LReLU) activation function for feature map activation to compute the most distinctive deep features. Finally, three fully connected classification layers are used to classify brain tumors MRI into normal and tumorous. The performance of the proposed TumorResNet architecture is evaluated on a standard Kaggle brain tumor MRI dataset for brain tumor detection (BTD), which contains brain tumor and normal MR images. The proposed model achieved a good accuracy of 99.33% for BTD. These experimental results, including the cross-dataset setting, validate the superiority of the TumorResNet model over the contemporary frameworks. This study offers an automated BTD method that aids in the early diagnosis of brain cancers. This procedure has a substantial impact on improving treatment options and patient survival.

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“…The spread of false information concerning COVID-19 poses a severe risk to public health [ 22 ]. Roozenbeek et al [ 23 ] investigate common misconceptions regarding the virus and look into the factors that influence people's willingness to accept the most widely spread falsehoods.…”

Section: Introductionmentioning

confidence: 99%

A Natural Language Processing (NLP) Evaluation on COVID-19 Rumour Dataset Using Deep Learning Techniques

Fatima

Shaikh²,

Riaz

et al. 2022

Computational Intelligence and Neuroscience

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Context and Background: Since December 2019, the coronavirus (COVID-19) epidemic has sparked considerable alarm among the general community and significantly affected societal attitudes and perceptions. Apart from the disease itself, many people suffer from anxiety and depression due to the disease and the present threat of an outbreak. Due to the fast propagation of the virus and misleading/fake information, the issues of public discourse alter, resulting in significant confusion in certain places. Rumours are unproven facts or stories that propagate and promote sentiments of prejudice, hatred, and fear. Objective. The study’s objective is to propose a novel solution to detect fake news using state-of-the-art machines and deep learning models. Furthermore, to analyse which models outperformed in detecting the fake news. Method. In the research study, we adapted a COVID-19 rumours dataset, which incorporates rumours from news websites and tweets, together with information about the rumours. It is important to analyse data utilizing Natural Language Processing (NLP) and Deep Learning (DL) approaches. Based on the accuracy, precision, recall, and the f1 score, we can assess the effectiveness of the ML and DL algorithms. Results. The data adopted from the source (mentioned in the paper) have collected 9200 comments from Google and 34,779 Twitter postings filtered for phrases connected with COVID-19-related fake news. Experiment 1. The dataset was assessed using the following three criteria: veracity, stance, and sentiment. In these terms, we have different labels, and we have applied the DL algorithms separately to each term. We have used different models in the experiment such as (i) LSTM and (ii) Temporal Convolution Networks (TCN). The TCN model has more performance on each measurement parameter in the evaluated results. So, we have used the TCN model for the practical implication for better findings. Experiment 2. In the second experiment, we have used different state-of-the-art deep learning models and algorithms such as (i) Simple RNN; (ii) LSTM + Word Embedding; (iii) Bidirectional + Word Embedding; (iv) LSTM + CNN-1D; and (v) BERT. Furthermore, we have evaluated the performance of these models on all three datasets, e.g., veracity, stance, and sentiment. Based on our second experimental evaluation, the BERT has a superior performance over the other models compared.

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A Novel CovidDetNet Deep Learning Model for Effective COVID-19 Infection Detection Using Chest Radiograph Images

Cited by 31 publications

References 58 publications

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

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

A Robust End-to-End Deep Learning-Based Approach for Effective and Reliable BTD Using MR Images

A Natural Language Processing (NLP) Evaluation on COVID-19 Rumour Dataset Using Deep Learning Techniques

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