ArticleApplied soft computing2021
A deep-learning based multimodal system for Covid-19 diagnosis using breathing sounds and chest X-ray images.
Article in Applied soft computing, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
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Who cites it
20 citing papers in PubMed, 66 citations in OpenAlex.
- Respiratory Disease Detection: A Systematic Review of AI-Based Approaches, from Audio and Visual Unimodal Methods to Multimodal Integration.Diagnostics (Basel, Switzerland) · 2026Review
- Machine learning and artificial intelligence for delirium prediction with Electronic Health Records (EHR): a scoping review.BMC medical informatics and decision making · 2026Article
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- Deep Learning for Pneumonia Detection in Chest X-ray Images: A Comprehensive Survey.Journal of imaging · 2024Review
- Challenges issues and future recommendations of deep learning techniques for SARS-CoV-2 detection utilising X-ray and CT images: a comprehensive review.PeerJ. Computer science · 2024Article
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- Dung Beetle Optimization with Deep Feature Fusion Model for Lung Cancer Detection and Classification.Cancers · 2023Article
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- An Artificial Intelligence-as-a-Service Architecture for deep learning model embodiment on low-cost devices: A case study of COVID-19 diagnosis.Applied soft computing · 2023Article
- COVID-19 and pneumonia diagnosis from chest X-ray images using convolutional neural networks.Network modeling and analysis in health informatics and bioinformatics · 2023Article
- Cov-Net: A computer-aided diagnosis method for recognizing COVID-19 from chest X-ray images via machine vision.Expert systems with applications · 2022Article
- Ensemble multimodal deep learning for early diagnosis and accurate classification of COVID-19.Computers & electrical engineering : an international journal · 2022Article
- Chest X ray and cough sample based deep learning framework for accurate diagnosis of COVID-19.Computers & electrical engineering : an international journal · 2022Article
- A Deep Ensemble Neural Network with Attention Mechanisms for Lung Abnormality Classification Using Audio Inputs.Sensors (Basel, Switzerland) · 2022Article
- COVID-19 prognosis using limited chest X-ray images.Applied soft computing · 2022Article
- Fully automatic deep convolutional approaches for the analysis of COVID-19 using chest X-ray images.Applied soft computing · 2022Article
- Detection of COVID-19 in smartphone-based breathing recordings: A pre-screening deep learning tool.PloS one · 2022Article
- Graphene-based temperature, humidity, and strain sensor: A review on progress, characterization, and potential applications during Covid-19 pandemic.Sensors international · 2022Article
- Interactive framework for Covid-19 detection and segmentation with feedback facility for dynamically improved accuracy and trust.PloS one · 2022Article
- An optimal cascaded recurrent neural network for intelligent COVID-19 detection using Chest X-ray images.Applied soft computing · 2021Article
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Authors and funding
8 authors at 4 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Covid-19 has become a deadly pandemic claiming more than three million lives worldwide. SARS-CoV-2 causes distinct pathomorphological alterations in the respiratory system, thereby acting as a biomarker to aid its diagnosis. A multimodal framework (Ai-CovScan) for Covid-19 detection using breathing sounds, chest X-ray (CXR) images, and rapid antigen test (RAnT) is proposed. Transfer Learning approach using existing deep-learning Convolutional Neural Network (CNN) based on Inception-v3 is combined with Multi-Layered Perceptron (MLP) to develop the CovScanNet model for reducing false-negatives. This model reports a preliminary accuracy of 80% for the breathing sound analysis, and 99.66% Covid-19 detection accuracy for the curated CXR image dataset. Based on Ai-CovScan, a smartphone
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.