Related references
Note: Only part of the references are listed.
Article
Engineering, Biomedical
Aparajita Nanda et al.
Summary: Early-stage diagnosis is crucial for curing brain tumors, and existing classification schemes using machine learning and deep learning have limitations. This paper proposes a new hybrid approach that combines saliency-K-means segmentation with social spider optimization algorithm in radial basis neural network for efficient classification. The proposed method is validated on standard datasets and achieves high accuracy.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2023)
Article
Engineering, Biomedical
Swagatika Devi et al.
Summary: This article studies the impact of brain magnetic resonance (MR) image quality on classification performance and uses deep convolutional neural networks (DCNN) to train and classify distorted MR images. Through comprehensive evaluation on multiple datasets, it is proven that these models can be used for assisting clinicians.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2023)
Article
Biology
Aryan Verma et al.
Summary: This study proposes a novel Android application that detects COVID-19 infection from chest CT scans using a highly efficient and accurate deep learning algorithm. It also creates an attention heatmap to show the regions of infection in the lungs. The results show that the system achieves high accuracy and efficiency in the early diagnosis of COVID-19.
COMPUTERS IN BIOLOGY AND MEDICINE
(2022)
Article
Biology
J. L. Gayathri et al.
Summary: This paper presents a computer-aided detection model utilizing chest X-ray images for combating the COVID-19 pandemic. By utilizing pre-trained networks, sparse autoencoder, and a Feed Forward Neural Network (FFNN), the model achieves accurate detection of COVID-19.
COMPUTERS IN BIOLOGY AND MEDICINE
(2022)
Article
Chemistry, Analytical
Ejaz Khan et al.
Summary: Recent technological developments have allowed deep learning techniques to be applied in various domains, including the medical field for disease classification and detection. This study proposes a deep learning-based technique for classifying COVID-19 infection from other non-COVID-19 infections. By fine-tuning the deep learning models and optimizing hyperparameters, the performance of the models is significantly improved. The proposed technique is evaluated using multiple performance parameters, and it outperforms other models in terms of accuracy.
Article
Medical Laboratory Technology
Michael M. Franklin et al.
Summary: A convolutional neural network (CNN) can accurately differentiate between autoimmune gastritis and Helicobacter pylori gastritis (HPG), with results consistent with those of gastrointestinal pathologists.
ARCHIVES OF PATHOLOGY & LABORATORY MEDICINE
(2022)
Article
Chemistry, Multidisciplinary
Abeer A. AbdElhamid et al.
Summary: In this work, a multi-level diagnostic framework based on transfer learning is proposed for accurate detection of COVID-19 using X-ray scans. The framework utilizes deep learning architecture with Xception pre-trained model for feature extraction and classification. The evaluation results show that the framework achieves high test accuracy and other performance metrics when using different activation functions, thresholds, and optimizers.
APPLIED SCIENCES-BASEL
(2022)
Article
Immunology
Carlos K. H. Wong et al.
Summary: Early initiation of remdesivir in hospitalized patients with moderate COVID-19, who do not require oxygen therapy, is associated with significantly shorter time to clinical improvement, low viral load and positive IgG antibody, a shorter length of hospital stay, and a significantly lower risk of in-hospital death.
CLINICAL INFECTIOUS DISEASES
(2022)
Article
Computer Science, Software Engineering
Muhammad Junaid Umer et al.
Summary: COVID-19 is rapidly spreading globally, causing devastating impact on the world economy and public health. Early detection and artificial intelligence play vital roles in the pandemic. A proposed method utilizing X-ray imaging achieved a 99.0% accuracy using two-phase deep feature extraction and quantum transfer learning model.
CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
(2022)
Article
Computer Science, Hardware & Architecture
Xiaole Fan et al.
Summary: A parallel bi-branch model based on Transformer module and Convolutional Neural Network module is proposed for extracting features from COVID-19 CT images, achieving significant improvement in classification accuracy. This model provides a new method for the diagnosis of COVID-19 and promotes the development of real-time diagnosis, contributing to saving lives.
Article
Biology
Veerraju Gampala et al.
Summary: This article introduces a method for classifying COVID-19 chest x-ray images using machine learning techniques, and improves the deep belief network method by utilizing hyper parameter tuning with the hosted cuckoo optimization algorithm. The proposed method achieves significant improvements in accuracy, precision, and F-score, and is validated through comparison with other methods.
ELECTROMAGNETIC BIOLOGY AND MEDICINE
(2022)
Article
Computer Science, Artificial Intelligence
Siyuan Lu et al.
Summary: The study developed a computer-aided diagnosis system based on artificial intelligence to automatically identify COVID-19 in chest computed tomography images. By combining neighboring aware representation and ILR graph, a novel COVID-19 classification architecture was proposed.
INTERNATIONAL JOURNAL OF INTELLIGENT SYSTEMS
(2022)
Article
Computer Science, Artificial Intelligence
Aksh Garg et al.
Summary: This paper utilizes 20 convolutional neural networks to classify patients as COVID-19 positive, healthy, or suffering from other pulmonary infections based on chest CT scans. The study finds that the EfficientNet-B5 model performs the best, offering a rapid and accurate diagnostic for COVID-19.
EXPERT SYSTEMS WITH APPLICATIONS
(2022)
Article
Computer Science, Information Systems
Vinayakumar Ravi et al.
Summary: This study proposes a new method for COVID-19 classification using pre-trained models and deep learning, which involves feature extraction, dimensionality reduction, feature fusion, and stacked ensemble meta-classifier. The method shows better performance than existing methods and can be used for point-of-care diagnosis by healthcare professionals.
MULTIMEDIA SYSTEMS
(2022)
Article
Computer Science, Information Systems
Rahul Kumar et al.
Summary: COVID-19 is a viral disease that can be detected using chest X-ray images. This study proposes a new model that utilizes machine learning and deep learning techniques to classify COVID-19, pneumonia, and normal cases, achieving promising results.
MULTIMEDIA TOOLS AND APPLICATIONS
(2022)
Article
Computer Science, Artificial Intelligence
Javaria Amin et al.
Summary: This study investigates the use of quantum machine learning and classical machine learning for analyzing COVID-19 images. The proposed model achieved high precision and accuracy on two datasets, showing better results compared to existing research in the field.
COGNITIVE COMPUTATION
(2022)
Article
Computer Science, Artificial Intelligence
R. Nandhini Abirami et al.
Summary: The COVID-19 pandemic has rapidly spread globally, posing challenges with false negatives in medical testing. This study proposes a COVID-19 GAN approach that uses generative adversarial networks for medical image synthesis, aiming to accelerate testing processes and prevent false negatives. By performing synthetic data augmentation and training a deep learning model using generated COVID-19 positive chest X-ray images, an accuracy rate of 99.78% was achieved in classifying chest X-ray images.
INTERNATIONAL JOURNAL OF UNCERTAINTY FUZZINESS AND KNOWLEDGE-BASED SYSTEMS
(2022)
Article
Mathematical & Computational Biology
Muhammad Attique Khan et al.
Summary: Detection and classification of COVID-19 using chest X-ray images is a current hot research topic in medical image analysis. Artificial intelligence techniques, especially deep learning and explainable AI, play a significant role in improving the accuracy and efficiency of diagnosing this infection.
COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE
(2022)
Article
Chemistry, Multidisciplinary
Naeem Ullah et al.
Summary: To detect COVID-19 cases quickly and accurately, a CovidDetNet model using chest radiograph images is proposed. This deep learning-based approach automatically and reliably detects COVID-19, achieving a high accuracy in experiments.
APPLIED SCIENCES-BASEL
(2022)
Article
Engineering, Multidisciplinary
Yilmaz Kaya et al.
Summary: Declared as a pandemic disease, COVID-19 has had significant effects on public health and millions of people's lives. Artificial intelligence methods, especially deep-learning-based approaches, have been effectively used for the detection of COVID-19. In this study, a new approach using x-ray images and the angle transform (AT) method is proposed for COVID-19 detection. The proposed approach achieves a high classification accuracy of 98.97% using a hybrid deep learning model of GoogleNet and LSTM. The results highlight the success of the proposed method for COVID-19 detection using chest x-ray images.
MEASUREMENT SCIENCE AND TECHNOLOGY
(2022)
Review
Medicine, General & Internal
Sudipta Roy et al.
Summary: This review addresses the current paradigm, the potential for new scientific discoveries, the technological state of preparation, the potential for supervised machine learning (SML) prospects in various healthcare sectors, and ethical issues. It evaluates the effectiveness and potential for innovation of various learning-based automation in healthcare, and emphasizes the requirement for explainable artificial intelligence (AI) in healthcare. Furthermore, it presents a study of medical imaging analysis and explores the new developments in healthcare through AI and SML.
Article
Public, Environmental & Occupational Health
Ameer Hamza et al.
Summary: This article introduces a deep learning-based computer-controlled technique for diagnosing COVID-19. By incorporating chest X-ray images into the diagnosis process and enhancing and processing the images using a series of algorithms, features are extracted in two deep learning networks built from scratch and classified using a fusion technique. Experimental results demonstrate a higher accuracy compared to existing techniques.
FRONTIERS IN PUBLIC HEALTH
(2022)
Article
Anatomy & Morphology
Javeria Amin et al.
Summary: A three-phase model is proposed for COVID-19 detection in CT images, achieving a global segmentation accuracy of 0.96 and 0.97 for classification through denoising, segmentation, and deep learning processes.
MICROSCOPY RESEARCH AND TECHNIQUE
(2022)
Article
Mathematical & Computational Biology
Muhammad Attique Khan et al.
Summary: A method for diagnosing and classifying COVID-19 using chest X-ray images is proposed by combining deep learning and explainable AI techniques. Experimental results show that this method outperforms other approaches in terms of accuracy.
COMPUTATIONAL INTELLIGENCE AND NEUROSCIENCE
(2022)
Article
Computer Science, Artificial Intelligence
Dalia Ezzat et al.
Summary: In this paper, a novel approach called GSA-DenseNet121-COVID-19 is proposed, utilizing DenseNet121 architecture and the gravitational search algorithm (GSA) to achieve a high level of accuracy in diagnosing COVID-19. The obtained results showed a classification accuracy of 98.38% on the test set, demonstrating the efficacy of the proposed approach.
APPLIED SOFT COMPUTING
(2021)
Article
Mathematics, Interdisciplinary Applications
Emtiaz Hussain et al.
Summary: A novel CNN model called CoroDet was proposed for automatic detection of COVID-19 using raw chest X-ray and CT scan images in this study. The model outperformed existing techniques in terms of classification accuracy, providing a solution to the issue of scarcity of COVID-19 testing kits.
CHAOS SOLITONS & FRACTALS
(2021)
Article
Computer Science, Interdisciplinary Applications
Panagiotis Marentakis et al.
Summary: This study investigated the classification of NSCLC into AC and SCC using different techniques, with LSTM + Inception showing superior performance and outperforming expert evaluations.
MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING
(2021)
Article
Computer Science, Software Engineering
Nandhini Abirami et al.
Summary: The study proposes a CNN and GAN-based framework for automated classification and segmentation of lung infections in COVID-19 lung CT images. The proposed model outperforms existing methods in accuracy and segmentation results, achieving precise boundaries and high segmentation accuracy.
INTERNATIONAL JOURNAL OF DATA WAREHOUSING AND MINING
(2021)
Article
Radiology, Nuclear Medicine & Medical Imaging
Shuai Wang et al.
Summary: This study utilized artificial intelligence to extract radiological features from CT images of COVID-19, achieving timely and accurate diagnosis of the disease, demonstrating the feasibility of using AI for disease control.
EUROPEAN RADIOLOGY
(2021)
Review
Radiology, Nuclear Medicine & Medical Imaging
Isabella Castiglioni et al.
Summary: This review focuses on the challenges associated with developing AI applications as clinical decision support systems in the context of biomedical research and healthcare services. It outlines the architectural characteristics of machine learning (ML)/radiomics and deep learning (DL) approaches, and discusses specific challenges such as data curation, interpretability of AI models, and the choice between ML and DL for medical imaging applications.
PHYSICA MEDICA-EUROPEAN JOURNAL OF MEDICAL PHYSICS
(2021)
Article
Computer Science, Artificial Intelligence
Shui-Hua Wang et al.
Summary: The study aimed to develop an advanced AI system for COVID-19 classification based on chest CT images. By utilizing different algorithms and pretrained models, researchers successfully created the CCSHNet model, which can effectively detect COVID-19 and other lung infectious diseases.
INFORMATION FUSION
(2021)
Article
Medical Laboratory Technology
Rolf F. Barth et al.
Summary: The review compares three coronavirus diseases, noting similarities and differences in clinical presentation, pathology, immune response, and prognosis. COVID-19 has caused a worldwide pandemic with a high number of cases and fatalities, but the development of vaccines offers hope for eventual control of the disease.
ARCHIVES OF PATHOLOGY & LABORATORY MEDICINE
(2021)
Article
Engineering, Biomedical
Samritika Thakur et al.
Summary: The research uses X-ray and CT images to introduce a deep learning strategy based on CNN to automatically detect Covid-19. With high accuracy in both binary and multiclass classifications, the proposed model can assist healthcare workers in quickly identifying patients.
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
(2021)
Article
Computer Science, Artificial Intelligence
Ghulam Muhammad et al.
Summary: This paper proposes a new model for COVID-19 screening, achieving 92.5% precision and 93.2% retrieval rate through experiments with lung ultrasound images and video datasets, which are considerably higher than existing CNN versions.
INFORMATION FUSION
(2021)
Article
Engineering, Multidisciplinary
Atinc Yilmaz
Summary: COVID-19 has been described as a global pandemic which poses a significant threat to life. A multi-channel CNN method has been proposed in this study to automatically diagnose COVID-19, reducing the diagnosis time effectively.
JOURNAL OF THE FACULTY OF ENGINEERING AND ARCHITECTURE OF GAZI UNIVERSITY
(2021)
Article
Computer Science, Artificial Intelligence
Rajit Nair et al.
Summary: This study developed an automatic model based on deep learning that can predict COVID-19 using chest CT scans, achieving good detection performance on a large dataset.
TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES
(2021)
Article
Computer Science, Artificial Intelligence
A. Helen Victoria et al.
Summary: Deep learning in artificial intelligence is effective in computer vision, natural language processing, and audio recognition. Hyperparameter tuning is crucial for training model performance, and Bayesian optimization algorithm enhances model performance effectively.
Article
Radiology, Nuclear Medicine & Medical Imaging
Vruddhi Shah et al.
Summary: Early and accurate diagnosis of COVID-19 is crucial for pandemic control. Utilizing deep learning techniques to analyze CT scan images can assist doctors in quickly screening for COVID-19.
EMERGENCY RADIOLOGY
(2021)
Article
Radiology, Nuclear Medicine & Medical Imaging
Wei-cai Dai et al.
CANADIAN ASSOCIATION OF RADIOLOGISTS JOURNAL-JOURNAL DE L ASSOCIATION CANADIENNE DES RADIOLOGISTES
(2020)
Article
Medicine, General & Internal
Na Zhu et al.
NEW ENGLAND JOURNAL OF MEDICINE
(2020)
Article
Computer Science, Artificial Intelligence
N. Dong et al.
APPLIED SOFT COMPUTING
(2020)
Article
Critical Care Medicine
Laurent Zieleskiewicz et al.
INTENSIVE CARE MEDICINE
(2020)
Article
Computer Science, Information Systems
Irfan Ullah Khan et al.
Article
Computer Science, Information Systems
Hyunghun Cho et al.
Review
Microbiology
Jie Cui et al.
NATURE REVIEWS MICROBIOLOGY
(2019)
Proceedings Paper
Computer Science, Information Systems
Ishrat Zahan Mukti et al.
2019 4TH INTERNATIONAL CONFERENCE ON ELECTRICAL INFORMATION AND COMMUNICATION TECHNOLOGY (EICT)
(2019)
Article
Automation & Control Systems
Armin Cheraghalipour et al.
ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE
(2018)
Article
Radiology, Nuclear Medicine & Medical Imaging
PJ Littrup et al.
Article
Computer Science, Artificial Intelligence
N Kwak et al.
IEEE TRANSACTIONS ON NEURAL NETWORKS
(2002)
