Deep convolutional neural networks for <scp>COVID‐19</scp> automatic diagnosis

Emara, Heba M.; Shoaib, Mohamed R.; Elwekeil, Mohamed; El‐Shafai, Walid; Taha, Taha E.; El‐Fishawy, Adel S.; El‐Rabaie, El‐Sayed M.; Alshebeili, Saleh A.; Dessouky, Moawad I.; El‐Samie, Fathi E. Abd

doi:10.1002/jemt.23713

Cited by 20 publications

(15 citation statements)

References 38 publications

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“… 2021 ) Xception model X-ray 97.97 CONVCOVID (Emara et al. 2021b ) CNN ResNet101 model X-ray 93 99 Transfer learning-based (Wang et al. 2021 ) A modified inception TL model CT 89.5 FPN (Song et al.…”

Section: Discussion and Comparison With The-state-of-the-art Methodsmentioning

confidence: 99%

“…Emara et al. ( 2021b ) used CNN models with various learning procedures for COVID-19 diagnosis. Firstly, a CNN-based TL algorithm was used to automatically diagnose COVID-19 from X-ray images with various training and testing ratios.…”

Section: Related Workmentioning

confidence: 99%

“…TL has led to good classification results in different applications (Emara et al. 2021a , b ). Hence, it is recommended in this paper.…”

Section: Introductionmentioning

confidence: 99%

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Hybrid classification structures for automatic COVID-19 detection

Shoaib

Emara

Elwekeil

et al. 2022

J Ambient Intell Human Comput

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Section: Discussion and Comparison With The-state-of-the-art Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Hybrid classification structures for automatic COVID-19 detection

Shoaib

Emara

Elwekeil

et al. 2022

J Ambient Intell Human Comput

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“…Such techniques have achieved great progress, when used in many aspects of medicine, especially in the diagnosis of epileptic seizures. In many fields, such as anomaly detection from medical signals and images, feature learning, target monitoring, and recognition; DL has achieved great advances 10–12 …”

Section: Introductionmentioning

confidence: 99%

Deep‐learning‐based seizure detection and prediction from electroencephalography signals

Ibrahim

Emara

El‐Shafai

et al. 2022

Numer Methods Biomed Eng

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About 50 million people around the world are affected by epilepsy disorders of different kinds. Any person, of any age, gender, race, or class, may be affected by epilepsy. In addition, epilepsy seizures can also vary in frequency of occurrence. Such seizures sometimes cause cognitive disorders, which may lead to physical injury of the patients. 1 Epilepsy is recognized by the World Health Organization (WHO) as a public health concern because of its physical and psychological consequences. Moreover, epilepsy may lead to premature death, loss of work productivity, and increased healthcare needs and expenditure. 2 For diagnosing epileptic seizures, distinct screening techniques have been developed; including Electroencephalography (EEG), positron emission tomography, magneto encephalography, and magnetic resonance imaging. EEG signals are characterized by being easily acquired with portable devices. 3 EEG can be defined as an electrophysiological exploration method by which electrical activities of the brain are measured using electrodes fixed on the scalp. 4 These electrodes may be bulky for patients. Utilization of EEG signals for diagnosing epilepsy is time-and effort-consuming; as epileptologists have to screen EEG signals minute by minute. Furthermore, human error is inevitable. Hence, a computer-based diagnosis, by which epileptic seizures can be early detected, is expected to help the patients. [5][6][7][8][9] Artificial intelligence covers several areas and includes several branches such as Machine Learning (ML) and Deep Learning (DL).Conventional ML algorithms, including feature extraction and classification, were formerly used before the appearance of DL. Hand-crafted features limit the performance of the classification algorithms, but deep features are preferred due to their better representation of signals and images. Such techniques have achieved great progress, when used in many aspects of medicine, especially in the diagnosis of epileptic seizures. In many fields, such as anomaly detection from medical signals and images, feature learning, target monitoring, and recognition; DL has achieved great advances. [10][11][12] In this paper, we propose an efficient strategy for both seizure detection and prediction from medical EEG signals. Three models are presented for the classification task. Two of them are patient-specific, while the third one is patient non-specific. EEG signals for epilepsy patients can be divided into three states: normal (inter-ictal), ictal (seizure), and pre-ictal which represents the period of 30-60 min before the ictal state. 13 We assumed in this paper that the pre-ictal state occurs 30 min before the ictal state. The two-class classification is implemented between normal and pre-ictal activities for seizure prediction and between normal and ictal activities for seizure detection. A more generalized threeclass classification framework is considered to identify all EEG signal activities. For the first two proposed models, the spectrogram estimation process is performed on EEG si...

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“…On the other hand, serological testing is conducted to locate the antibodies generated to fight against the COVID‐19 virus. For this examination, the blood sample of the patient is required to detect the infection level in the blood with mild or no signs (Emara et al, 2021 ; Kushwaha et al, 2020 ). These antibodies are found anywhere in the body of a patient recovered from COVID‐19.…”

Section: Introductionmentioning

confidence: 99%

COVID‐DAI: A novel framework forCOVID‐19 detection and infection growth estimation using computed tomography images

Nazir

Nawaz

Javed

et al. 2022

Microscopy Res & Technique

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The COVID‐19 pandemic is spreading at a fast pace around the world and has a high mortality rate. Since there is no proper treatment of COVID‐19 and its multiple variants, for example, Alpha, Beta, Gamma, and Delta, being more infectious in nature are affecting millions of people, further complicates the detection process, so, victims are at the risk of death. However, timely and accurate diagnosis of this deadly virus can not only save the patients from life loss but can also prevent them from the complex treatment procedures. Accurate segmentation and classification of COVID‐19 is a tedious job due to the extensive variations in its shape and similarity with other diseases like Pneumonia. Furthermore, the existing techniques have hardly focused on the infection growth estimation over time which can assist the doctors to better analyze the condition of COVID‐19‐affected patients. In this work, we tried to overcome the shortcomings of existing studies by proposing a model capable of segmenting, classifying the COVID‐19 from computed tomography images, and predicting its behavior over a certain period. The framework comprises four main steps: (i) data preparation, (ii) segmentation, (iii) infection growth estimation, and (iv) classification. After performing the pre‐processing step, we introduced the DenseNet‐77 based UNET approach. Initially, the DenseNet‐77 is used at the Encoder module of the UNET model to calculate the deep keypoints which are later segmented to show the coronavirus region. Then, the infection growth estimation of COVID‐19 per patient is estimated using the blob analysis. Finally, we employed the DenseNet‐77 framework as an end‐to‐end network to classify the input images into three classes namely healthy, COVID‐19‐affected, and pneumonia images. We evaluated the proposed model over the COVID‐19‐20 and COVIDx CT‐2A datasets for segmentation and classification tasks, respectively. Furthermore, unlike existing techniques, we performed a cross‐dataset evaluation to show the generalization ability of our method. The quantitative and qualitative evaluation confirms that our method is robust to both COVID‐19 segmentation and classification and can accurately predict the infection growth in a certain time frame.

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Deep convolutional neural networks for COVID‐19 automatic diagnosis

Cited by 20 publications

References 38 publications

Hybrid classification structures for automatic COVID-19 detection

Hybrid classification structures for automatic COVID-19 detection

Deep‐learning‐based seizure detection and prediction from electroencephalography signals

COVID‐DAI: A novel framework forCOVID‐19 detection and infection growth estimation using computed tomography images

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