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Article
Computer Science, Artificial Intelligence
Moloud Abdar et al.
Summary: The COVID-19 pandemic poses a major threat to human health, making the development of computer-aided detection systems a priority. This study introduces a new deep learning feature fusion model called UncertaintyFuseNet, which accurately classifies CT scan and X-ray images. The results demonstrate the efficiency and robustness of the model.
INFORMATION FUSION
(2023)
Article
Mathematical & Computational Biology
S. V. Kogilavani et al.
Summary: SARS-CoV-2 is a novel virus responsible for the COVID-19 pandemic, and CT scans combined with deep learning algorithms can be used for COVID-19 detection. The research findings show that the VGG16 architecture has a higher diagnostic accuracy compared to other architectures.
COMPUTATIONAL AND MATHEMATICAL METHODS IN MEDICINE
(2022)
Article
Geochemistry & Geophysics
Lianlei Shan et al.
Summary: Recently, FCN-based networks have achieved impressive success in semantic segmentation of natural images. However, in high-resolution remote sensing image segmentation, there is a considerable gap in accuracy compared to natural images. The key to accurate segmentation is context, and effective networks can obtain large contexts. To address the limitations of networks designed for natural images, targeted improvements including unit fusion and cross-level fusion were proposed. Experimental results on Deepglobe dataset showed significant improvements in segmentation performance.
IEEE GEOSCIENCE AND REMOTE SENSING LETTERS
(2022)
Article
Engineering, Electrical & Electronic
Emine Cengil et al.
Summary: This article focuses on obtaining effective features for image classification applications to enhance classification performance, using the method of concatenating features and applying it to COVID-19 related datasets, achieving high classification accuracies. It also points out the difficulty in diagnosing COVID-19 disease and the need for rapid detection, increasing the demand for computer-aided deep learning models.
INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY
(2022)
Article
Computer Science, Information Systems
Anupam Das
Summary: COVID-19 is a major health calamity in the 21st century, with developing countries facing delays in recognizing cases. The combination of medical images and deep learning classifiers offer more hopeful results with high accuracy in predicting and recognizing COVID-19 cases.
MULTIMEDIA TOOLS AND APPLICATIONS
(2022)
Article
Health Care Sciences & Services
Hanane Allioui et al.
Summary: In this study, a new mask extraction method based on multi-agent deep reinforcement learning (DRL) was introduced and applied to the diagnosis of COVID-19. Experimental validation showed that the method can accurately extract masks of COVID-19 infected areas and achieved good results in pathogenic diagnostic tests and time saving.
JOURNAL OF PERSONALIZED MEDICINE
(2022)
Article
Biology
Omneya Attallah et al.
Summary: Pediatric medulloblastomas (MBs), the most common malignant brain tumors in children, are heterogenous and challenging to classify accurately based on histopathological images. This study combines textural analysis and deep learning techniques to improve the subtype identification of pediatric MBs. The automated pipeline proposed in this study shows an increased accuracy in classification compared to previous methods, providing a powerful tool for individualized therapies and identification of high-risk complications in children.
Article
Engineering, Biomedical
Abhijit Bhattacharyya et al.
Summary: In this study, a new method based on X-ray images is proposed, utilizing segmentation and feature extraction of lung images along with artificial intelligence and machine learning models for classifying COVID-19, pneumonia, and normal lung images, achieving a high testing classification accuracy of 96.6%.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2022)
Article
Automation & Control Systems
Sadia Showkat et al.
Summary: Due to the effect of COVID-19 on pulmonary tissues, Chest X-ray (CXR) and Computed Tomography (CT) images have become the preferred imaging methods for early detection of COVID-19 infections. The use of Convolutional Neural Networks (CNN) and Transfer Learning (TL) approach, specifically the ResNet architecture, has shown effectiveness in accurately classifying pneumonia cases from CXR images. The customized ResNet model achieved high global accuracy, precision, specificity, and sensitivity, providing reliable analysis of CXR images for clinical decision-making.
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
(2022)
Article
Automation & Control Systems
Narin Aslan et al.
Summary: This study aims to improve the accuracy of trained deep Convolutional Neural Networks (CNN) in automatically diagnosing COVID-19 on X-ray images by applying Iterative Neighborhood Component Analysis (INCA) and Iterative ReliefF (IRF) feature selection methods. After performing thirteen different deep CNN experiments and evaluations, the VGG16 network with INCA feature selection showed the highest predictive value. The proposed method achieved high performance criteria, enhancing the classification accuracy of COVID-19.
CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS
(2022)
Article
Biology
Mohamed Loey et al.
Summary: This study proposes a Bayesian optimization-based convolutional neural network model for classifying chest X-ray images of COVID-19 artifacts, achieving high accuracy on a large-scale dataset.
COMPUTERS IN BIOLOGY AND MEDICINE
(2022)
Review
Biology
Nandhini Subramanian et al.
Summary: This paper surveys the currently available deep learning methods for detecting coronavirus infection in lung images. The available methodologies, datasets, and evaluation metrics are summarized to assist future researchers. The evaluation metrics used by these methods are comprehensively compared.
COMPUTERS IN BIOLOGY AND MEDICINE
(2022)
Article
Biology
Omneya Attallah
Summary: This paper proposes a new method, ECG-BiCoNet, for diagnosing COVID-19 using electrocardiogram (ECG) data. The results show that ECG data can be used to accurately diagnose COVID-19, which helps clinicians with automatic diagnosis and overcomes limitations of manual diagnosis.
COMPUTERS IN BIOLOGY AND MEDICINE
(2022)
Article
Computer Science, Hardware & Architecture
Xin Zhang et al.
Summary: In this study, a deep learning network-based framework for COVID-19 diagnosis is proposed. By improving AlexNet and introducing three classifiers, three novel models are obtained. Among them, DC-Net-R performs the best on a private dataset and outperforms other existing algorithms.
JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY
(2022)
Article
Computer Science, Information Systems
Vipul Kumar Singh et al.
Summary: The novel coronavirus outbreak has caused a global pandemic, requiring increased testing and isolation measures. A fine-tuned deep learning model inspired by MobileNet V2 architecture has been developed to improve accuracy and speed of diagnosis, with experimental results showing its superiority.
MULTIMEDIA TOOLS AND APPLICATIONS
(2022)
Article
Computer Science, Information Systems
Rohit Kundu et al.
Summary: The COVID-19 pandemic has had a significant global impact, resulting in numerous deaths. Traditional testing methods have limitations, prompting the development of an automated and fast COVID-19 screening method based on deep learning techniques, which has demonstrated excellent performance.
MULTIMEDIA TOOLS AND APPLICATIONS
(2022)
Article
Chemistry, Analytical
Omneya Attallah
Summary: Accurate and rapid diagnosis of COVID-19 is crucial for controlling its spread, reducing lockdown restrictions, and alleviating the burden on healthcare systems. This study introduces a novel automated diagnostic tool based on ECG data, utilizing deep learning models to achieve high accuracy in diagnosing COVID-19 at binary and multiclass levels.
Article
Radiology, Nuclear Medicine & Medical Imaging
H. Nasiri et al.
Summary: This study developed a method for diagnosing COVID-19 from chest X-ray images using DenseNet169 deep neural network to extract features and Extreme Gradient Boosting algorithm for classification. The proposed method was found to be more accurate and faster than existing methods, with acceptable performance in detecting COVID-19 cases from X-ray images.
Article
Engineering, Biomedical
Anubhav Sharma et al.
Summary: This paper introduces an advanced deep learning method, COVDC-Net, for identifying and classifying cases of novel coronavirus infection, highlighting its importance for the current healthcare system.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2022)
Article
Medicine, General & Internal
Omneya Attallah et al.
Summary: Lung and colon cancers are leading causes of mortality and morbidity. This study proposes a framework based on multiple lightweight deep learning models to detect these cancers at an early stage, achieving a high accuracy of 99.6% through feature reduction and fusion techniques.
Article
Computer Science, Artificial Intelligence
Omneya Attallah et al.
Summary: The quick and accurate diagnosis of COVID-19 is crucial for preventing its spread and improving treatment outcomes. This study proposes a deep learning-based pipeline called CoviWavNet, which utilizes 3D multiview CT slices and spectral-temporal information to enhance the diagnostic accuracy of COVID-19.
APPLIED SOFT COMPUTING
(2022)
Article
Computer Science, Artificial Intelligence
Seyed Mohammad Jafar Jalali et al.
Summary: This paper proposes a method based on convolutional neural networks and K-nearest neighbors classifier to detect COVID-19 disease. To improve the accuracy, an improved competitive swarm optimizer is used for hyperparameter tuning. Experimental results show that the proposed method outperforms other models in the literature in terms of performance.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Information Systems
Cinare Oguz et al.
Summary: This study aims to reduce the duration and amount of COVID-19 transmission by shortening the diagnosis time of patients using Computed Tomography (CT). Deep learning models and classification methods were employed to develop a decision support system for radiologists. By extracting deep features and evaluating their performance, the study found that the combination of ResNet-50 and SVM achieved the best accuracy, F1-score, and AUC value. The high performance of this system suggests its potential as an auxiliary tool for diagnosing COVID-19.
INFORMATION PROCESSING & MANAGEMENT
(2022)
Article
Health Care Sciences & Services
Omneya Attallah
Summary: With the current health crisis caused by the COVID-19 pandemic, patients' preference for limited contact with doctors or clinicians has led to the development of computer-aided facial diagnosis systems. This study introduces FaceDisNet, a novel system that utilizes deep learning techniques and a new public dataset to diagnose single and multiple diseases accurately without physical contact with patients. The high accuracy achieved by FaceDisNet demonstrates its reliability and potential for assisting physicians in manual diagnosis.
Article
Radiology, Nuclear Medicine & Medical Imaging
Lin Yang et al.
Summary: The automatic recognition of COVID-19 diseases is important for relieving healthcare staff from the burden of screening for COVID-19 infection. This study proposes three deep learning architectures to detect COVID-19 infections accurately from chest CT images. The results show that the proposed method significantly improves the recognition of COVID-19 infections with high accuracy, outperforming most of the current COVID-19 recognition models. Additionally, a web application has been built for users to upload their chest CT scans and obtain automatic COVID-19 results.
Article
Health Care Sciences & Services
Omneya Attallah
Summary: The accurate and rapid detection of novel coronavirus is crucial to prevent its spread, and artificial intelligence techniques can aid in this process. This study proposes a computer-assisted diagnostic framework based on deep learning and texture-based radiomics approaches. By fusing deep features from multiple convolutional neural networks, the diagnostic accuracy is improved. The performance of this framework allows radiologists to achieve fast and accurate diagnosis of coronavirus.
Article
Health Care Sciences & Services
Walaa Gouda et al.
Summary: This study proposes two novel deep learning methods for detecting COVID-19 using chest X-ray images, which achieve reliable diagnosis through preprocessing and utilizing a pre-trained model. The proposed system outperforms existing methods in various metrics, as demonstrated on public benchmark datasets.
Article
Radiology, Nuclear Medicine & Medical Imaging
H. Nasiri et al.
Summary: The study focused on detecting COVID-19 cases from chest X-ray images using the DenseNet169 Deep Neural Network and Extreme Gradient Boosting algorithm. Results showed higher accuracy and faster speed compared to existing methods, making it a valuable tool for initial detection and faster diagnosis of the disease.
Article
Biochemistry & Molecular Biology
Aayush Jaiswal et al.
Summary: This study aims to utilize pre-trained deep learning architecture for the detection and diagnosis of COVID-19, proposing a Deep Transfer Learning (DTL) model based on DenseNet201. Experimental results show that the proposed model outperforms competitive approaches in COVID-19 chest CT scan images.
JOURNAL OF BIOMOLECULAR STRUCTURE & DYNAMICS
(2021)
Article
Computer Science, Artificial Intelligence
Amin Ullah et al.
Summary: This paper proposes a lightweight deep learning-assisted framework for activity recognition, which detects and tracks humans in surveillance videos using CNN models, and learns temporal changes in frame sequences for activity recognition using DS-GRU. Experimental results demonstrate the efficiency of this technique for real-time surveillance applications.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Zhou Tao et al.
Summary: This study proposes an ensemble deep learning model for rapid detection of COVID-19 from CT images, achieving higher accuracy and sensitivity compared to individual classifiers. This approach can better meet the requirements for rapid detection of the novel coronavirus disease COVID-19.
APPLIED SOFT COMPUTING
(2021)
Article
Computer Science, Artificial Intelligence
Rajeev Kumar Singh et al.
Summary: The article introduces a novel deep learning solution using chest X-rays for rapid triaging of COVID-19 patients, addressing the issue of scarce testing resources for COVID-19 detection.
NEURAL COMPUTING & APPLICATIONS
(2021)
Article
Engineering, Biomedical
Xiao Qi et al.
Summary: The outbreak of COVID-19 has highlighted the importance of effective diagnosis, with radiography such as chest X-rays containing valuable information about the virus and showing promise as an alternative diagnostic method. A novel multi-feature convolutional neural network architecture was designed for improved classification of COVID-19 from X-ray images, with enhanced images proving to enhance diagnostic accuracy. The proposed method achieved improved results compared to single-feature CNN, emphasizing the significance of local phase-based X-ray image enhancement.
INTERNATIONAL JOURNAL OF COMPUTER ASSISTED RADIOLOGY AND SURGERY
(2021)
Article
Biology
Sertan Serte et al.
Summary: COVID-19, a new type of pneumonia coronavirus, has caused many infections and deaths worldwide. AI techniques can assist radiologists in quickly and accurately detecting COVID-19 infection on CT scans, improving diagnostic efficiency.
COMPUTERS IN BIOLOGY AND MEDICINE
(2021)
Article
Biology
Nguyen Quoc Khanh Le et al.
Summary: This study evaluated the efficiency of an XGBoost-based radiomics model in classifying transcriptome subtypes in glioblastoma patients, identifying 13 radiomics features through two-level feature selection techniques for predictive accuracies exceeding 70%. The model's performance surpassed that of previous works, suggesting the potential of XGBoost and feature selection analysis as a promising combination for further research on radiomics-based GBM models.
COMPUTERS IN BIOLOGY AND MEDICINE
(2021)
Article
Chemistry, Analytical
Muhammad Attique Khan et al.
Summary: In this study, deep learning techniques were used to diagnose COVID-19 patients on medical images, with an automated technique proposed for classification and optimization. Experimental results achieved an accuracy of 98%, showing improved performance of the proposed scheme.
Review
Environmental Sciences
Tarik Alafif et al.
Summary: Machine learning (ML) and deep learning (DL) have been widely used in various aspects of our daily lives and have played a crucial role in addressing the global outbreak of Coronavirus (COVID-19). The COVID-19 pandemic, caused by the SARS-CoV-2 virus, has rapidly spread worldwide, leading to international outbreaks. The fight against COVID-19 involves the collaboration of most countries, companies, and scientific research institutions.
INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH
(2021)
Article
Medicine, General & Internal
Omneya Attallah
Summary: Medulloblastoma (MB) is a dangerous malignant pediatric brain tumor. Precise diagnosis is essential. The computer-aided diagnosis system MB-AI-His combines deep learning and texture analysis for automatic diagnosis of pediatric MB and its subtypes from histopathological images efficiently and reliably.
Article
Computer Science, Artificial Intelligence
Omneya Attallah et al.
Summary: The article proposes a CADx system named Gastro-CADx for classifying multiple GI diseases using DL techniques. The system consists of three stages: extracting spatial features using CNN, extracting temporal-frequency and spatial-frequency features using DWT and DCT, and fusing multiple feature combinations for optimal output selection.
PEERJ COMPUTER SCIENCE
(2021)
Article
Chemistry, Multidisciplinary
Najam-ur Rehman et al.
Summary: Chest diseases such as pneumonia, asthma, edema, and COVID-19 can be dangerous and deadly. Differentiating COVID-19 from other chest diseases is challenging, and a framework for detecting 15 types of chest diseases via chest X-ray modality was proposed to improve the accuracy of COVID-19 detection.
APPLIED SCIENCES-BASEL
(2021)
Article
Medicine, General & Internal
Omneya Attallah
Summary: Retinopathy of Prematurity (ROP) affects preterm neonates and could lead to blindness. Deep Learning (DL) can assist ophthalmologists in diagnosing ROP, and the automated diagnostic tool DIAROP achieved an accuracy of 93.2%.
Article
Engineering, Multidisciplinary
Junjie Xing et al.
Summary: An automatic detection method based on convolutional neural networks is proposed in this paper and its detection performance is evaluated and compared with other models, showing that the method has better performance in real-time automatic detection of workpiece surface defects.
Article
Computer Science, Artificial Intelligence
Omneya Attallah et al.
Summary: Breast cancer is a common cancer affecting females globally, and late diagnosis can lead to severe consequences. The Histo-CADx system proposed in this study successfully improves the accuracy of breast cancer diagnosis through two fusion stages.
PEERJ COMPUTER SCIENCE
(2021)
Article
Engineering, Biomedical
Danial Sharifrazi et al.
Summary: By combining convolutional neural network (CNN), support vector machine (SVM) and Sobel filter, utilizing high pass filtering with CNN-SVM + Sobel model leads to automated detection of COVID-19 with high classification accuracy, sensitivity, and specificity.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2021)
Article
Environmental Sciences
Jose L. Domingo
Summary: The origin of COVID-19 has been a topic of much debate, with some supporting a natural origin while others suggesting a possible unnatural origin. Clear evidence confirming the intermediate host of SARS-CoV-2 is still lacking, and investigations into the origin are crucial in preventing similar future pandemics.
ENVIRONMENTAL RESEARCH
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Chemistry, Analytical
Muhammad Umair et al.
Summary: The research utilized deep learning models for early detection and classification of COVID-19. By employing transfer learning techniques with fine tuning, four pre-trained models were successfully trained, with DenseNet-121 outperforming the others in terms of both accuracy and prediction.
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Mathematical & Computational Biology
Omneya Attallah
Summary: Childhood medulloblastoma (MB) is a common malignant tumor, and early and accurate classification is crucial. CoMB-Deep combines deep learning and texture analysis feature extraction techniques to effectively classify MB and improve diagnostic accuracy.
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