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Article
Computer Science, Artificial Intelligence
Alaa S. Al-Waisy et al.
Summary: The outbreaks of the COVID-19 epidemic have increased the pressure on healthcare and medical systems worldwide. Chest radiography imaging has been shown to be an effective screening technique for diagnosing the COVID-19 epidemic. To reduce pressure on radiologists, a hybrid deep learning framework called COVID-CheXNet has been developed for fast and accurate diagnosis of COVID-19 virus in chest X-ray images. The system achieved high detection accuracy and efficiency, making it a potential tool for real clinical centers.
Article
Radiology, Nuclear Medicine & Medical Imaging
Jennifer Dhont et al.
Summary: The purpose of this study was to investigate the extent to which dataset bias has influenced the performance of convolutional neural networks (CNNs) for COVID-19 screening based on chest radiography (CXR). The results showed that when trained on a combinatory dataset, CNNs tend to learn the origin of the CXRs rather than the presence or absence of disease. The dataset bias was found to originate from differences in overall pixel values rather than embedded text or symbols.
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)
Review
Computer Science, Artificial Intelligence
Zaid Abdi Alkareem Alyasseri et al.
Summary: COVID-19, caused by SARS-CoV-2, has become a pandemic infecting over 152 million people in more than 216 countries. This review paper summarizes over 200 studies from December 2019 to April 2021 on COVID-19 diagnosis using machine learning and deep learning techniques, highlighting SVM and CNN as widely used mechanisms for diagnosing and predicting outbreaks. Accuracy, sensitivity, and specificity are commonly used measurements in previous studies.
Article
Computer Science, Artificial Intelligence
Dheyaa Ahmed Ibrahim et al.
Summary: This study investigates the use of hybrid deep learning methods for the quick and accurate identification of individuals infected with COVID-19 based on their lung CT images. A reliable COVID-19 prediction network is proposed, starting with lung CT scan image segmentation and ending with disease prediction. The system combines pre-trained models to extract affective features and achieved a 95% accuracy rate on a publicly available dataset. The developed model shows effectiveness in accurately screening COVID-19 CT images and could serve as an additional diagnostic tool for clinical professionals.
Article
Nanoscience & Nanotechnology
Aliza Tariq et al.
Summary: Artificial intelligence is a subfield of computer science that focuses on developing intelligent machines capable of performing tasks similar to humans. With advancements, AI has expanded its applications in healthcare, gaming, and smart devices. This study conducted a systematic literature review to analyze the current state of healthcare software measurement using AI. The research concluded that software automation is crucially needed in healthcare.
JOURNAL OF NANOMATERIALS
(2022)
Article
Health Care Sciences & Services
Karrar Hameed Abdulkareem et al.
Summary: COVID-19 is a global health issue with high fatality rate. This study developed a diagnostic system for COVID-19 using deep learning techniques, specifically convolutional neural networks, stacked autoencoders, and deep neural networks. The system achieved high accuracy in classifying CT images and outperformed existing state-of-the-art models in detecting the virus.
JOURNAL OF HEALTHCARE ENGINEERING
(2022)
Article
Health Care Sciences & Services
Mazhar Javed Awan et al.
Summary: The study aimed to identify three ACL tear conditions using machine learning models and compared their performance. It took into account the imbalanced data distribution, which is often challenging for machine learning techniques. The results showed that after hyperparameter adjustment and oversampling, the four models achieved high accuracy on the balanced ACL dataset.
JOURNAL OF HEALTHCARE ENGINEERING
(2022)
Article
Health Care Sciences & Services
Sania Shamim et al.
Summary: The COVID-19 pandemic has had a devastating impact globally, and this research proposes a more accurate and reliable segmentation approach for identifying ground glass opacity in COVID-19 CT images.
JOURNAL OF HEALTHCARE ENGINEERING
(2022)
Article
Chemistry, Multidisciplinary
Ramzi Mahmoudi et al.
Summary: This study developed a diagnosis system based on deep learning techniques to detect and quantify COVID-19 infection and pneumonia screening using CT imaging. A pre-processing method and U-net architecture were used for image segmentation, and a CNN architecture was used for classification and volume reconstruction to achieve accurate diagnosis.
APPLIED SCIENCES-BASEL
(2022)
Article
Medicine, General & Internal
Mazhar Javed Awan et al.
Summary: The study proposed a deep learning method for early detection of ACL injury via automatic MRI, achieving high accuracy and evaluation metrics. It utilized a ResNet-14 CNN architecture with class balancing and data augmentation, showing differentiation and evaluation results for healthy tear, partial tear, and fully ruptured tear.
Article
Computer Science, Artificial Intelligence
Tej Bahadur Chandra et al.
Summary: This study introduces an automatic COVID screening (ACoS) system for identifying nCOVID-19 infected patients, which utilizes radiomic texture descriptors and a majority vote based classifier ensemble of five benchmark supervised classification algorithms. The system shows promising performance in the validation phase, with statistically significant results confirmed through Friedman post-hoc multiple comparisons and z-test statistics.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Computer Science, Artificial Intelligence
Aras M. Ismael et al.
Summary: COVID-19 is a novel virus that has led to a global pandemic, with daily increases in cases and deaths. Deep learning approaches, including deep feature extraction and fine-tuning of pretrained convolutional neural networks, show potential in detecting COVID-19 based on chest X-ray images.
EXPERT SYSTEMS WITH APPLICATIONS
(2021)
Article
Environmental Sciences
Mazhar Javed Awan et al.
Summary: COVID-19 spreads rapidly and has proven challenging to detect and cure early. Deep learning has greatly contributed to medical research, offering new possibilities for diagnosis techniques. Different deep learning models have been applied to COVID-19 chest X-ray images with high accuracy.
INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH
(2021)
Article
Materials Science, Multidisciplinary
Jamal N. Hasoon et al.
Summary: This study proposes a method for early detection and classification of COVID-19 through image processing using X-ray images. Different combinations of feature extraction operators and classifiers were tested, with the LBP-KNN model proving to be the most effective.
RESULTS IN PHYSICS
(2021)
Article
Computer Science, Information Systems
Laraib Aslam Haafza et al.
Summary: The paper discusses the study on the application of big data during the COVID-19 pandemic, conducts a systematic review of relevant literature, and highlights the technological advancements in the field. The research findings successfully address the nature of the COVID-19 crisis, providing a reference for the research community to further address the pandemic.
Article
Computer Science, Information Systems
Mazhar Javed Awan et al.
Summary: With the rapid increase in malware, a deep learning-based method for image classification of malware was proposed to meet the demand for detecting and neutralizing these malicious agents. Experimental evaluations show that the model has high performance and can be used for malware detection.
Article
Health Care Sciences & Services
Mazhar Javed Awan et al.
Summary: The study aims to automatically and efficiently identify ACL tears using deep learning, comparing and evaluating two variants of CNN models. The customized CNN model showed better results than the standard model in identifying ACL tears, indicating potential for further improvement and applications in detecting and segmenting other ligament parts.
JOURNAL OF PERSONALIZED MEDICINE
(2021)
Article
Computer Science, Artificial Intelligence
Amit Kumar Das et al.
Summary: This study introduces a solution based on deep convolutional neural networks to detect COVID-19 positive patients using chest X-ray images. Multiple CNN models are utilized and combined through a weighted average ensembling technique for prediction.
PATTERN ANALYSIS AND APPLICATIONS
(2021)
Article
Chemistry, Multidisciplinary
Marwa Ben Jabra et al.
Summary: The paper explores the efficiency of using deep learning classifiers in diagnosing COVID-19 from chest X-ray images and conducts a comprehensive comparison study among 16 state-of-the-art classifiers. It found that using the Majority Voting approach is an adequate strategy to adopt and may achieve an average accuracy up to 99.314% in this task.
APPLIED SCIENCES-BASEL
(2021)
Article
Chemistry, Multidisciplinary
Abdullah Mujahid et al.
Summary: A lightweight model based on YOLO v3 and DarkNet-53 convolutional neural networks is proposed for gesture recognition. The model achieved high accuracy even in complex environments and successfully detected gestures in low-resolution picture mode.
APPLIED SCIENCES-BASEL
(2021)
Article
Computer Science, Information Systems
Approach Saleh Albahli et al.
Summary: Social media serves as the main source of information, contributing to fear and uncertainty due to inaccurate information, leading to panic among people. Studies indicate that Gulf countries' sentiments towards the pandemic vary between neutral, positive, and negative emotions.
CMC-COMPUTERS MATERIALS & CONTINUA
(2021)
Article
Medical Informatics
Jose Daniel Lopez-Cabrera et al.
Summary: The scientific community is working together to address the COVID-19 pandemic, using medical images and artificial intelligence for disease classification, though errors need to be corrected.
HEALTH AND TECHNOLOGY
(2021)
Article
Orthopedics
Shashank Vaid et al.
INTERNATIONAL ORTHOPAEDICS
(2020)
Article
Instruments & Instrumentation
Saleh Albahli et al.
JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY
(2020)
Article
Computer Science, Artificial Intelligence
Shervin Minaee et al.
MEDICAL IMAGE ANALYSIS
(2020)
Article
Respiratory System
Liqa A. Rousan et al.
BMC PULMONARY MEDICINE
(2020)
Article
Computer Science, Information Systems
Morteza Heidari et al.
INTERNATIONAL JOURNAL OF MEDICAL INFORMATICS
(2020)
Article
Computer Science, Artificial Intelligence
Neha Gianchandani et al.
JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING
(2020)
Article
Computer Science, Artificial Intelligence
Goncalo Marques et al.
APPLIED SOFT COMPUTING
(2020)
Article
Computer Science, Interdisciplinary Applications
Asif Iqbal Khan et al.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2020)
Article
Computer Science, Interdisciplinary Applications
Luca Brunese et al.
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
(2020)
Article
Computer Science, Interdisciplinary Applications
Mohammad Khalid Pandit et al.
INTERNATIONAL JOURNAL OF PERVASIVE COMPUTING AND COMMUNICATIONS
(2020)
Article
Computer Science, Information Systems
Muhammad E. H. Chowdhury et al.
Article
Computer Science, Information Systems
Yasir Ali et al.
Article
Mathematical & Computational Biology
Saad Albawi et al.
COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE
(2018)
