A Novel Approach to Detect COVID-19: Enhanced Deep Learning Models with Convolutional Neural Networks

Abdulrahman, Awf; Baykara, Muhammet

doi:10.3390/app12189325

Cited by 20 publications

(6 citation statements)

References 32 publications

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“…Ramadhan et al [13] built a VGG16-CNN to classify three public datasets that consist of chest X-ray images of COVID-19 patients. Binary classification was conducted on three datasets, and the model achieved 97% accuracy on the first dataset, 98.73% on the second dataset, and the highest accuracy was achieved using the third dataset, i.e., 99.76%.…”

Section: Covid-19mentioning

confidence: 99%

Joint Diagnosis of Pneumonia, COVID-19, and Tuberculosis from Chest X-ray Images: A Deep Learning Approach

Ahmed,

Rahman,

AlGhamdi

et al. 2023

Diagnostics

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Pneumonia, COVID-19, and tuberculosis are some of the most fatal and common lung diseases in the current era. Several approaches have been proposed in the literature for the diagnosis of individual diseases, since each requires a different feature set altogether, but few studies have been proposed for a joint diagnosis. A patient being diagnosed with one disease as negative may be suffering from the other disease, and vice versa. However, since said diseases are related to the lungs, there might be a likelihood of more than one disease being present in the same patient. In this study, a deep learning model that is able to detect the mentioned diseases from the chest X-ray images of patients is proposed. To evaluate the performance of the proposed model, multiple public datasets have been obtained from Kaggle. Consequently, the proposed model achieved 98.72% accuracy for all classes in general and obtained a recall score of 99.66% for Pneumonia, 99.35% for No-findings, 98.10% for Tuberculosis, and 96.27% for COVID-19, respectively. Furthermore, the model was tested using unseen data from the same augmented dataset and was proven to be better than state-of-the-art studies in the literature in terms of accuracy and other metrics.

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Section: Covid-19mentioning

confidence: 99%

Joint Diagnosis of Pneumonia, COVID-19, and Tuberculosis from Chest X-ray Images: A Deep Learning Approach

Ahmed,

Rahman,

AlGhamdi

et al. 2023

Diagnostics

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“…The architecture is widely used in computer vision applications such as object detection and image segmentation (Popescu et al, 2022). The architecture for VGG-16 (Ramadhan & Baykara, 2022) is shown in Figure 4A, and it was the most used for insect detection and classification tasks. The convolutional layers are responsible for extracting features from the input image, while the pooling layers reduce the spatial dimensions of the feature maps to reduce computation time.…”

Section: Neural Network Used In Insect Detection Segmentation and Cla...mentioning

confidence: 99%

New trends in detection of harmful insects and pests in modern agriculture using artificial neural networks. a review

Popescu,

Dinca,

Ichim

et al. 2023

Front. Plant Sci.

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Modern and precision agriculture is constantly evolving, and the use of technology has become a critical factor in improving crop yields and protecting plants from harmful insects and pests. The use of neural networks is emerging as a new trend in modern agriculture that enables machines to learn and recognize patterns in data. In recent years, researchers and industry experts have been exploring the use of neural networks for detecting harmful insects and pests in crops, allowing farmers to act and mitigate damage. This paper provides an overview of new trends in modern agriculture for harmful insect and pest detection using neural networks. Using a systematic review, the benefits and challenges of this technology are highlighted, as well as various techniques being taken by researchers to improve its effectiveness. Specifically, the review focuses on the use of an ensemble of neural networks, pest databases, modern software, and innovative modified architectures for pest detection. The review is based on the analysis of multiple research papers published between 2015 and 2022, with the analysis of the new trends conducted between 2020 and 2022. The study concludes by emphasizing the significance of ongoing research and development of neural network-based pest detection systems to maintain sustainable and efficient agricultural production.

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“…The researchers collected CXR images from various sources (from different classes of chests humans) to detect COVID-19 disease. On the other hand, the study [27] used a pre-trained approach called VGG16-CNN to detect COVID-19 cases using CXR images. While the study achieved an accuracy of 97.50% for multiple classifications, the authors tested the models and found varying proportions, such as an F1-score rate, precision rate, recall rate, and overall accuracy.…”

Section: Related Workmentioning

confidence: 99%

Fast Hybrid Deep Neural Network for Diagnosis of COVID-19 using Chest X-Ray Images

Ali¹,

Zghal²,

Hariri³

et al. 2023

IJACSA

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In the last three years, the coronavirus (COVID-19) pandemic put healthcare systems worldwide under tremendous pressure. Imaging techniques, such as Chest X-Ray (CXR) images, play an essential role in diagnosing many diseases (for example, COVID-19). Recently, intelligent systems (Machine Learning (ML) and Deep Learning (DL)) have been widely utilized to identify COVID-19 from other upper respiratory diseases (such as viral pneumonia and lung opacity). Nevertheless, identifying COVID-19 from the CXR images is challenging due to similar symptoms. To improve the diagnosis of COVID-19 using CXR images, this article proposes a new deep neural network model called Fast Hybrid Deep Neural Network (FHDNN). FHDNN consists of various convolutional layers and various dense layers. In the beginning, we preprocessed the dataset, extracted the best features, and expanded it. Then, we converted it from two dimensions to one dimension to reduce training speed and hardware requirements. The experimental results demonstrate that preprocessing and feature expansion before applying FHDNN lead to better detection accuracy and reduced speedy execution. Furthermore, the model FHDNN outperformed the counterparts by achieving an accuracy of 99.9%, recall of 99.9%, F1-Score has 99.9%, and precision of 99.9% for the detection and classification of COVID-19. Accordingly, FHDNN is more reliable and can be considered a robust and faster model in COVID-19 detection.

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A Novel Approach to Detect COVID-19: Enhanced Deep Learning Models with Convolutional Neural Networks

Cited by 20 publications

References 32 publications

Joint Diagnosis of Pneumonia, COVID-19, and Tuberculosis from Chest X-ray Images: A Deep Learning Approach

Joint Diagnosis of Pneumonia, COVID-19, and Tuberculosis from Chest X-ray Images: A Deep Learning Approach

New trends in detection of harmful insects and pests in modern agriculture using artificial neural networks. a review

Fast Hybrid Deep Neural Network for Diagnosis of COVID-19 using Chest X-Ray Images

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