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Article ; Online: COVID-19 cough sound symptoms classification from scalogram image representation using deep learning models.

Loey, Mohamed / Mirjalili, Seyedali

2021 Volume 139, Page(s) 105020

Abstract: Deep Learning shows promising performance in diverse fields and has become an emerging technology in Artificial Intelligence. Recent visual recognition is based on the ranking of photographs and the finding of artefacts in those images. The aim of this ... ...

Abstract	Deep Learning shows promising performance in diverse fields and has become an emerging technology in Artificial Intelligence. Recent visual recognition is based on the ranking of photographs and the finding of artefacts in those images. The aim of this research is to classify the different cough sounds of COVID-19 artefacts in the signals of altered real-life environments. The introduced model takes into consideration two major steps. The first step is the transformation phase from sound to image that is optimized by the scalogram technique. The second step involves feature extraction and classification based on six deep transfer models (GoogleNet, ResNet18, ResNet50, ResNet101, MobileNetv2, and NasNetmobile). The dataset used contains 1457 (755 of COVID-19 and 702 of healthy) wave cough sounds. Although our recognition model performs the best, its accuracy only reaches 94.9% based on SGDM optimizer. The accuracy is promising enough for a wide set of labeled cough data to test the potential for generalization. The outcomes show that ResNet18 is the most stable model to classify the cough sounds from a limited dataset with a sensitivity of 94.44% and a specificity of 95.37%. Finally, a comparison of the research with a similar analysis is made. It is observed that the proposed model is more reliable and accurate than any current models. Cough research precision is promising enough to test the ability for extrapolation and generalization.
MeSH term(s)	Artificial Intelligence ; COVID-19 ; Cough ; Deep Learning ; Humans ; Neural Networks, Computer ; SARS-CoV-2
Language	English
Publishing date	2021-11-10
Publishing country	United States
Document type	Journal Article
ZDB-ID	127557-4
ISSN	1879-0534 ; 0010-4825
ISSN (online)	1879-0534
ISSN	0010-4825
DOI	10.1016/j.compbiomed.2021.105020
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Article ; Online: Bayesian-based optimized deep learning model to detect COVID-19 patients using chest X-ray image data.

Loey, Mohamed / El-Sappagh, Shaker / Mirjalili, Seyedali

Computers in biology and medicine

2022 Volume 142, Page(s) 105213

Abstract: Coronavirus Disease 2019 (COVID-19) is extremely infectious and rapidly spreading around the globe. As a result, rapid and precise identification of COVID-19 patients is critical. Deep Learning has shown promising performance in a variety of domains and ... ...

Abstract	Coronavirus Disease 2019 (COVID-19) is extremely infectious and rapidly spreading around the globe. As a result, rapid and precise identification of COVID-19 patients is critical. Deep Learning has shown promising performance in a variety of domains and emerged as a key technology in Artificial Intelligence. Recent advances in visual recognition are based on image classification and artefacts detection within these images. The purpose of this study is to classify chest X-ray images of COVID-19 artefacts in changed real-world situations. A novel Bayesian optimization-based convolutional neural network (CNN) model is proposed for the recognition of chest X-ray images. The proposed model has two main components. The first one utilizes CNN to extract and learn deep features. The second component is a Bayesian-based optimizer that is used to tune the CNN hyperparameters according to an objective function. The used large-scale and balanced dataset comprises 10,848 images (i.e., 3616 COVID-19, 3616 normal cases, and 3616 Pneumonia). In the first ablation investigation, we compared Bayesian optimization to three distinct ablation scenarios. We used convergence charts and accuracy to compare the three scenarios. We noticed that the Bayesian search-derived optimal architecture achieved 96% accuracy. To assist qualitative researchers, address their research questions in a methodologically sound manner, a comparison of research method and theme analysis methods was provided. The suggested model is shown to be more trustworthy and accurate in real world.
MeSH term(s)	Algorithms ; Artificial Intelligence ; Bayes Theorem ; COVID-19 ; Deep Learning ; Humans ; Radiography, Thoracic ; SARS-CoV-2 ; X-Rays
Language	English
Publishing date	2022-01-05
Publishing country	United States
Document type	Journal Article
ZDB-ID	127557-4
ISSN	1879-0534 ; 0010-4825
ISSN (online)	1879-0534
ISSN	0010-4825
DOI	10.1016/j.compbiomed.2022.105213
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Article: Retraction Note: A deep learning model and machine learning methods for the classification of potential coronavirus treatments on a single human cell.

Khalifa, Nour Eldeen M / Taha, Mohamed Hamed N / Manogaran, Gunasekaran / Loey, Mohamed

Journal of nanoparticle research : an interdisciplinary forum for nanoscale science and technology

2021 Volume 23, Issue 8, Page(s) 184

Abstract: This retracts the article DOI: 10.1007/s11051-020-05041-z.]. ...

Abstract	[This retracts the article DOI: 10.1007/s11051-020-05041-z.].
Language	English
Publishing date	2021-08-16
Publishing country	Netherlands
Document type	Retraction of Publication
ZDB-ID	2017013-0
ISSN	1572-896X ; 1388-0764
ISSN (online)	1572-896X
ISSN	1388-0764
DOI	10.1007/s11051-021-05266-6
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Article ; Online: A deep learning semantic segmentation architecture for COVID-19 lesions discovery in limited chest CT datasets.

Khalifa, Nour Eldeen M / Manogaran, Gunasekaran / Taha, Mohamed Hamed N / Loey, Mohamed

Expert systems

2021 Volume 39, Issue 6, Page(s) e12742

Abstract: During the epidemic of COVID-19, Computed Tomography (CT) is used to help in the diagnosis of patients. Most current studies on this subject appear to be focused on broad and private annotated data which are impractical to access from an organization, ... ...

Abstract	During the epidemic of COVID-19, Computed Tomography (CT) is used to help in the diagnosis of patients. Most current studies on this subject appear to be focused on broad and private annotated data which are impractical to access from an organization, particularly while radiologists are fighting the coronavirus disease. It is challenging to equate these techniques since they were built on separate datasets, educated on various training sets, and tested using different metrics. In this research, a deep learning semantic segmentation architecture for COVID-19 lesions detection in limited chest CT datasets will be presented. The proposed model architecture consists of the encoder and the decoder components. The encoder component contains three layers of convolution and pooling, while the decoder contains three layers of deconvolutional and upsampling. The dataset consists of 20 CT scans of lungs belongs to 20 patients from two sources of data. The total number of images in the dataset is 3520 CT scans with its labelled images. The dataset is split into 70% for the training phase and 30% for the testing phase. Images of the dataset are passed through the pre-processing phase to be resized and normalized. Five experimental trials are conducted through the research with different images selected for the training and the testing phases for every trial. The proposed model achieves 0.993 in the global accuracy, and 0.987, 0.799, 0.874 for weighted IoU, mean IoU and mean BF score accordingly. The performance metrics such as precision, sensitivity, specificity and F1 score strengthens the obtained results. The proposed model outperforms the related works which use the same dataset in terms of performance and IoU metrics.
Language	English
Publishing date	2021-05-31
Publishing country	England
Document type	Journal Article
ZDB-ID	2016958-9
ISSN	1468-0394 ; 0266-4720
ISSN (online)	1468-0394
ISSN	0266-4720
DOI	10.1111/exsy.12742
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Article: A comprehensive survey of recent trends in deep learning for digital images augmentation.

Khalifa, Nour Eldeen / Loey, Mohamed / Mirjalili, Seyedali

Artificial intelligence review

2021 Volume 55, Issue 3, Page(s) 2351–2377

Abstract: Deep learning proved its efficiency in many fields of computer science such as computer vision, image classifications, object detection, image segmentation, and more. Deep learning models primarily depend on the availability of huge datasets. Without the ...

Abstract	Deep learning proved its efficiency in many fields of computer science such as computer vision, image classifications, object detection, image segmentation, and more. Deep learning models primarily depend on the availability of huge datasets. Without the existence of many images in datasets, different deep learning models will not be able to learn and produce accurate models. Unfortunately, several fields don't have access to large amounts of evidence, such as medical image processing. For example. The world is suffering from the lack of COVID-19 virus datasets, and there is no benchmark dataset from the beginning of 2020. This pandemic was the main motivation of this survey to deliver and discuss the current image data augmentation techniques which can be used to increase the number of images. In this paper, a survey of data augmentation for digital images in deep learning will be presented. The study begins and with the introduction section, which reflects the importance of data augmentation in general. The classical image data augmentation taxonomy and photometric transformation will be presented in the second section. The third section will illustrate the deep learning image data augmentation. Finally, the fourth section will survey the state of the art of using image data augmentation techniques in the different deep learning research and application.
Language	English
Publishing date	2021-09-04
Publishing country	England
Document type	Journal Article
ZDB-ID	1479828-1
ISSN	1573-7462 ; 0269-2821
ISSN (online)	1573-7462
ISSN	0269-2821
DOI	10.1007/s10462-021-10066-4
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Article: A hybrid deep transfer learning model with machine learning methods for face mask detection in the era of the COVID-19 pandemic.

Loey, Mohamed / Manogaran, Gunasekaran / Taha, Mohamed Hamed N / Khalifa, Nour Eldeen M

Measurement : journal of the International Measurement Confederation

2020 Volume 167, Page(s) 108288

Abstract: The coronavirus COVID-19 pandemic is causing a global health crisis. One of the effective protection methods is wearing a face mask in public areas according to the World Health Organization (WHO). In this paper, a hybrid model using deep and classical ... ...

Abstract	The coronavirus COVID-19 pandemic is causing a global health crisis. One of the effective protection methods is wearing a face mask in public areas according to the World Health Organization (WHO). In this paper, a hybrid model using deep and classical machine learning for face mask detection will be presented. The proposed model consists of two components. The first component is designed for feature extraction using Resnet50. While the second component is designed for the classification process of face masks using decision trees, Support Vector Machine (SVM), and ensemble algorithm. Three face masked datasets have been selected for investigation. The Three datasets are the Real-World Masked Face Dataset (RMFD), the Simulated Masked Face Dataset (SMFD), and the Labeled Faces in the Wild (LFW). The SVM classifier achieved 99.64% testing accuracy in RMFD. In SMFD, it achieved 99.49%, while in LFW, it achieved 100% testing accuracy.
Keywords	covid19
Language	English
Publishing date	2020-07-28
Publishing country	England
Document type	Journal Article
ZDB-ID	2000550-7
ISSN	0263-2241
ISSN	0263-2241
DOI	10.1016/j.measurement.2020.108288
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Article: Deep Transfer Learning Models for Medical Diabetic Retinopathy Detection.

Khalifa, Nour Eldeen M / Loey, Mohamed / Taha, Mohamed Hamed N / Mohamed, Hamed Nasr Eldin T

Acta informatica medica : AIM : journal of the Society for Medical Informatics of Bosnia & Herzegovina : casopis Drustva za medicinsku informatiku BiH

2020 Volume 27, Issue 5, Page(s) 327–332

Abstract: Introduction: Diabetic retinopathy (DR) is the most common diabetic eye disease worldwide and a leading cause of blindness. The number of diabetic patients will increase to 552 million by 2034, as per the International Diabetes Federation (IDF).: Aim!# ...

Abstract	Introduction: Diabetic retinopathy (DR) is the most common diabetic eye disease worldwide and a leading cause of blindness. The number of diabetic patients will increase to 552 million by 2034, as per the International Diabetes Federation (IDF). Aim: With advances in computer science techniques, such as artificial intelligence (AI) and deep learning (DL), opportunities for the detection of DR at the early stages have increased. This increase means that the chances of recovery will increase and the possibility of vision loss in patients will be reduced in the future. Methods: In this paper, deep transfer learning models for medical DR detection were investigated. The DL models were trained and tested over the Asia Pacific Tele-Ophthalmology Society (APTOS) 2019 dataset. According to literature surveys, this research is considered one the first studies to use of the APTOS 2019 dataset, as it was freshly published in the second quarter of 2019. The selected deep transfer models in this research were AlexNet, Res-Net18, SqueezeNet, GoogleNet, VGG16, and VGG19. These models were selected, as they consist of a small number of layers when compared to larger models, such as DenseNet and InceptionResNet. Data augmentation techniques were used to render the models more robust and to overcome the overfitting problem. Results: The testing accuracy and performance metrics, such as the precision, recall, and F1 score, were calculated to prove the robustness of the selected models. The AlexNet model achieved the highest testing accuracy at 97.9%. In addition, the achieved performance metrics strengthened our achieved results. Moreover, AlexNet has a minimum number of layers, which decreases the training time and the computational complexity.
Language	English
Publishing date	2020-03-25
Publishing country	Bosnia and Herzegovina
Document type	Journal Article
ZDB-ID	2558601-4
ISSN	1986-5988 ; 0353-8109
ISSN (online)	1986-5988
ISSN	0353-8109
DOI	10.5455/aim.2019.27.327-332
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Article ; Online: Fighting against COVID-19: A novel deep learning model based on YOLO-v2 with ResNet-50 for medical face mask detection.

Loey, Mohamed / Manogaran, Gunasekaran / Taha, Mohamed Hamed N / Khalifa, Nour Eldeen M

Sustainable cities and society

2020 Volume 65, Page(s) 102600

Abstract: Deep learning has shown tremendous potential in many real-life applications in different domains. One of these potentials is object detection. Recent object detection which is based on deep learning models has achieved promising results concerning the ... ...

Abstract	Deep learning has shown tremendous potential in many real-life applications in different domains. One of these potentials is object detection. Recent object detection which is based on deep learning models has achieved promising results concerning the finding of an object in images. The objective of this paper is to annotate and localize the medical face mask objects in real-life images. Wearing a medical face mask in public areas, protect people from COVID-19 transmission among them. The proposed model consists of two components. The first component is designed for the feature extraction process based on the ResNet-50 deep transfer learning model. While the second component is designed for the detection of medical face masks based on YOLO v2. Two medical face masks datasets have been combined in one dataset to be investigated through this research. To improve the object detection process, mean IoU has been used to estimate the best number of anchor boxes. The achieved results concluded that the adam optimizer achieved the highest average precision percentage of 81% as a detector. Finally, a comparative result with related work has been presented at the end of the research. The proposed detector achieved higher accuracy and precision than the related work.
Keywords	covid19
Language	English
Publishing date	2020-11-12
Publishing country	Netherlands
Document type	Journal Article
ISSN	2210-6715
ISSN (online)	2210-6715
DOI	10.1016/j.scs.2020.102600
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Article: A deep learning model and machine learning methods for the classification of potential coronavirus treatments on a single human cell.

Khalifa, Nour Eldeen M / Taha, Mohamed Hamed N / Manogaran, Gunasekaran / Loey, Mohamed

publication RETRACTED

Journal of nanoparticle research : an interdisciplinary forum for nanoscale science and technology

2020 Volume 22, Issue 11, Page(s) 313

Abstract: Coronavirus pandemic is burdening healthcare systems around the world to the full capacity they can accommodate. There is an overwhelming need to find a treatment for this virus as early as possible. Computer algorithms and deep learning can participate ... ...

Abstract	Coronavirus pandemic is burdening healthcare systems around the world to the full capacity they can accommodate. There is an overwhelming need to find a treatment for this virus as early as possible. Computer algorithms and deep learning can participate positively by finding a potential treatment for SARS-CoV-2. In this paper, a deep learning model and machine learning methods for the classification of potential coronavirus treatments on a single human cell will be presented. The dataset selected in this work is a subset of the publicly online datasets available on RxRx.ai. The objective of this research is to automatically classify a single human cell according to the treatment type and the treatment concentration level. A DCNN model and a methodology are proposed throughout this work. The methodical idea is to convert the numerical features from the original dataset to the image domain and then fed them up into a DCNN model. The proposed DCNN model consists of three convolutional layers, three ReLU layers, three pooling layers, and two fully connected layers. The experimental results show that the proposed DCNN model for treatment classification (32 classes) achieved 98.05% in testing accuracy if it is compared with classical machine learning such as support vector machine, decision tree, and ensemble. In treatment concentration level prediction, the classical machine learning (ensemble) algorithm achieved 98.5% in testing accuracy while the proposed DCNN model achieved 98.2%. The performance metrics strengthen the obtained results from the conducted experiments for the accuracy of treatment classification and treatment concentration level prediction.
Keywords	covid19
Language	English
Publishing date	2020-10-17
Publishing country	Netherlands
Document type	Journal Article ; Retracted Publication
ZDB-ID	2017013-0
ISSN	1572-896X ; 1388-0764
ISSN (online)	1572-896X
ISSN	1388-0764
DOI	10.1007/s11051-020-05041-z
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Article: A deep transfer learning model with classical data augmentation and CGAN to detect COVID-19 from chest CT radiography digital images.

Loey, Mohamed / Manogaran, Gunasekaran / Khalifa, Nour Eldeen M

Neural computing & applications

2020 , Page(s) 1–13

Abstract: The Coronavirus disease 2019 (COVID-19) is the fastest transmittable virus caused by severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2). The detection of COVID-19 using artificial intelligence techniques and especially deep learning will help ... ...

Abstract	The Coronavirus disease 2019 (COVID-19) is the fastest transmittable virus caused by severe acute respiratory syndrome Coronavirus 2 (SARS-CoV-2). The detection of COVID-19 using artificial intelligence techniques and especially deep learning will help to detect this virus in early stages which will reflect in increasing the opportunities of fast recovery of patients worldwide. This will lead to release the pressure off the healthcare system around the world. In this research, classical data augmentation techniques along with Conditional Generative Adversarial Nets (CGAN) based on a deep transfer learning model for COVID-19 detection in chest CT scan images will be presented. The limited benchmark datasets for COVID-19 especially in chest CT images are the main motivation of this research. The main idea is to collect all the possible images for COVID-19 that exists until the very writing of this research and use the classical data augmentations along with CGAN to generate more images to help in the detection of the COVID-19. In this study, five different deep convolutional neural network-based models (AlexNet, VGGNet16, VGGNet19, GoogleNet, and ResNet50) have been selected for the investigation to detect the Coronavirus-infected patient using chest CT radiographs digital images. The classical data augmentations along with CGAN improve the performance of classification in all selected deep transfer models. The outcomes show that ResNet50 is the most appropriate deep learning model to detect the COVID-19 from limited chest CT dataset using the classical data augmentation with testing accuracy of 82.91%, sensitivity 77.66%, and specificity of 87.62%.
Keywords	covid19
Language	English
Publishing date	2020-10-26
Publishing country	England
Document type	Journal Article
ZDB-ID	1480526-1
ISSN	1433-3058 ; 0941-0643
ISSN (online)	1433-3058
ISSN	0941-0643
DOI	10.1007/s00521-020-05437-x
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