Can AI help in screening Viral and COVID-19 pneumonia?

Abstract

Coronavirus disease (COVID-19) is a pandemic disease, which has already caused thousands of causalities and infected several millions of people worldwide. Any technological tool enabling rapid screening of the COVID-19 infection with high accuracy can be crucially helpful to healthcare professionals. The main clinical tool currently in use for the diagnosis of COVID-19 is the Reverse transcription polymerase chain reaction (RT-PCR), which is expensive, less-sensitive and requires specialized medical personnel. X-ray imaging is an easily accessible tool that can be an excellent alternative in the COVID-19 diagnosis. This research was taken to investigate the utility of artificial intelligence (AI) in the rapid and accurate detection of COVID-19 from chest X-ray images. The aim of this paper is to propose a robust technique for automatic detection of COVID-19 pneumonia from digital chest X-ray images applying pre-trained deep-learning algorithms while maximizing the detection accuracy. A public database was created by the authors combining several public databases and also by collecting images from recently published articles. The database contains a mixture of 423 COVID-19, 1485 viral pneumonia, and 1579 normal chest X-ray images. Transfer learning technique was used with the help of image augmentation to train and validate several pre-trained deep Convolutional Neural Networks (CNNs). The networks were trained to classify two different schemes: i) normal and COVID-19 pneumonia; ii) normal, viral and COVID-19 pneumonia with and without image augmentation. The classification accuracy, precision, sensitivity, and specificity for both the schemes were 99.7%, 99.7%, 99.7% and 99.55% and 97.9%, 97.95%, 97.9%, and 98.8%, respectively.

12 pages, 9 Figures

References in corpus (10)

• ChestX-ray8: Hospital-scale Chest X-ray Database and Benchmarks on Weakly-Supervised Classification and Localization of Common Thorax Diseases
• Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?
• Covid-19: Automatic detection from X-Ray images utilizing Transfer Learning with Convolutional Neural Networks
• CoroNet: A deep neural network for detection and diagnosis of COVID-19 from chest x-ray images
• COVIDX-Net: A Framework of Deep Learning Classifiers to Diagnose COVID-19 in X-Ray Images
• Transfer Learning with Deep Convolutional Neural Network (CNN) for Pneumonia Detection using Chest X-ray
• Viral Pneumonia Screening on Chest X-ray Images Using Confidence-Aware Anomaly Detection
• An Intelligent and Low-cost Eye-tracking System for Motorized Wheelchair Control
• Advance Warning Methodologies for COVID-19 using Chest X-Ray Images
• COVID-MobileXpert: On-Device COVID-19 Patient Triage and Follow-up using Chest X-rays

Cited by in corpus (76)

• COVID-CT-Dataset: A CT Scan Dataset about COVID-19
• Reliable Tuberculosis Detection using Chest X-ray with Deep Learning, Segmentation and Visualization
• Improving performance of CNN to predict likelihood of COVID-19 using chest X-ray images with preprocessing algorithms
• Hybrid quantum convolutional neural networks model for COVID-19 prediction using chest X-Ray images
• Computer Vision For COVID-19 Control: A Survey
• Convolutional Sparse Support Estimator Based Covid-19 Recognition from X-ray Images
• Do Gradient Inversion Attacks Make Federated Learning Unsafe?
• Recent Progress in Transformer-based Medical Image Analysis
• COVID-19 Infection Map Generation and Detection from Chest X-Ray Images
• COVID-19 Chest CT Image Segmentation -- A Deep Convolutional Neural Network Solution
• Backbones-Review: Feature Extraction Networks for Deep Learning and Deep Reinforcement Learning Approaches
• COV-ECGNET: COVID-19 detection using ECG trace images with deep convolutional neural network
• Study of Different Deep Learning Approach with Explainable AI for Screening Patients with COVID-19 Symptoms: Using CT Scan and Chest X-ray Image Dataset
• Self-supervised deep convolutional neural network for chest X-ray classification
• Vision Transformer based COVID-19 Detection using Chest X-rays
• PDCOVIDNet: A Parallel-Dilated Convolutional Neural Network Architecture for Detecting COVID-19 from Chest X-Ray Images
• A Survey of Deep Learning Techniques for the Analysis of COVID-19 and their usability for Detecting Omicron
• Explainable AI, but explainable to whom?
• ECOVNet: An Ensemble of Deep Convolutional Neural Networks Based on EfficientNet to Detect COVID-19 From Chest X-rays
• Diagnosis of COVID-19 Using Machine Learning and Deep Learning: A review
• Advance Warning Methodologies for COVID-19 using Chest X-Ray Images
• Survey on Adversarial Attack and Defense for Medical Image Analysis: Methods and Challenges
• COVID-19 detection from scarce chest x-ray image data using few-shot deep learning approach
• Medical Imaging and Computational Image Analysis in COVID-19 Diagnosis: A Review
• Interpretable CNN-Multilevel Attention Transformer for Rapid Recognition of Pneumonia from Chest X-Ray Images
• An Efficient Mixture of Deep and Machine Learning Models for COVID-19 and Tuberculosis Detection Using X-Ray Images in Resource Limited Settings
• QUCoughScope: An Artificially Intelligent Mobile Application to Detect Asymptomatic COVID-19 Patients using Cough and Breathing Sounds
• How does this interaction affect me? Interpretable attribution for feature interactions
• CVR-Net: A deep convolutional neural network for coronavirus recognition from chest radiography images
• COVID-MobileXpert: On-Device COVID-19 Patient Triage and Follow-up using Chest X-rays
• Combating COVID-19 using Generative Adversarial Networks and Artificial Intelligence for Medical Images: A Scoping Review
• COVID-19 detection using deep convolutional neural networks and binary-differential-algorithm-based feature selection on X-ray images
• Improving Representation of High-frequency Components for Medical Visual Foundation Models
• Deep learning in computed tomography pulmonary angiography imaging: a dual-pronged approach for pulmonary embolism detection
• Performance of GAN-based augmentation for deep learning COVID-19 image classification
• Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Images
• Reliable COVID-19 Detection Using Chest X-ray Images
• MultiFusionNet: Multilayer Multimodal Fusion of Deep Neural Networks for Chest X-Ray Image Classification
• Towards Interpretable Radiology Report Generation via Concept Bottlenecks using a Multi-Agentic RAG
• Spectral decoupling allows training transferable neural networks in medical imaging
• On the Composition and Limitations of Publicly Available COVID-19 X-Ray Imaging Datasets
• A methodology for training homomorphicencryption friendly neural networks
• M4CXR: Exploring Multi-task Potentials of Multi-modal Large Language Models for Chest X-ray Interpretation
• Early Screening of SARS-CoV-2 by Intelligent Analysis of X-Ray Images
• Exploring the Effect of Image Enhancement Techniques on COVID-19 Detection using Chest X-rays Images
• A Systematic Collection of Medical Image Datasets for Deep Learning
• Superpixel-based Knowledge Infusion in Deep Neural Networks for Image Classification
• Ensemble-CVDNet: A Deep Learning based End-to-End Classification Framework for COVID-19 Detection using Ensembles of Networks
• Benchmarking Foundation Models and Parameter-Efficient Fine-Tuning for Prognosis Prediction in Medical Imaging
• Ebola Optimization Search Algorithm (EOSA): A new metaheuristic algorithm based on the propagation model of Ebola virus disease
• COVIDX: Computer-aided diagnosis of Covid-19 and its severity prediction with raw digital chest X-ray images
• Privacy in Practice: Private COVID-19 Detection in X-Ray Images (Extended Version)
• Artificial Intelligence for COVID-19 Detection -- A state-of-the-art review
• CBM-RAG: Demonstrating Enhanced Interpretability in Radiology Report Generation with Multi-Agent RAG and Concept Bottleneck Models
• Exponential Pixelating Integral transform with dual fractal features for enhanced chest X-ray abnormality detection
• Survey on Computational Applications of Tensor Network Simulations
• MS-DCANet: A Novel Segmentation Network For Multi-Modality COVID-19 Medical Images
• One-Vote Veto: Semi-Supervised Learning for Low-Shot Glaucoma Diagnosis
• Novelty Detection via Robust Variational Autoencoding
• DPCOVID: Privacy-Preserving Federated Covid-19 Detection
• Deep Learning Models May Spuriously Classify Covid-19 from X-ray Images Based on Confounders
• CLOG-CD: Curriculum Learning based on Oscillating Granularity of Class Decomposed Medical Image Classification
• CIRCA: comprehensible online system in support of chest X-rays-based COVID-19 diagnosis
• DRR4Covid: Learning Automated COVID-19 Infection Segmentation from Digitally Reconstructed Radiographs
• Resource and data efficient self supervised learning
• TensorProjection Layer: A Tensor-Based Dimension Reduction Method in Deep Neural Networks
• Federated Contrastive Learning for Decentralized Unlabeled Medical Images
• Explanatory Analysis and Rectification of the Pitfalls in COVID-19 Datasets
• COVID-19 Detection through Deep Feature Extraction
• Advancement of Deep Learning in Pneumonia and Covid-19 Classification and Localization: A Qualitative and Quantitative Analysis
• Epanechnikov nonparametric kernel density estimation based feature-learning in respiratory disease chest X-ray images
• Clustering COVID-19 Lung Scans
• A Privacy-Preserving and Trustable Multi-agent Learning Framework
• Tiled sparse coding in eigenspaces for the COVID-19 diagnosis in chest X-ray images
• A Survey of Machine Learning Techniques for Detecting and Diagnosing COVID-19 from Imaging
• Recognition of COVID-19 Disease Utilizing X-Ray Imaging of the Chest Using CNN