ReviewComputerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society2021
Evaluation of deep learning approaches for identification of different corona-virus species and time series prediction.
Review in Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
8 citing papers in PubMed, 34 citations in OpenAlex.
- Deep learning techniques for using computed tomography imaging for hepatocellular carcinoma diagnosis, treatment and prognosis.World journal of gastroenterology · 2026Review
- The Applications of Nanopore Sequencing Technology in Animal and Human Virus Research.Viruses · 2024Review
- Robust Medical Diagnosis: A Novel Two-Phase Deep Learning Framework for Adversarial Proof Disease Detection in Radiology Images.Journal of imaging informatics in medicine · 2024Article
- Forecasting SARS-CoV-2 spike protein evolution from small data by deep learning and regression.Frontiers in systems biology · 2024Article
- Deep learning based hybrid prediction model for predicting the spread of COVID-19 in the world's most populous countries.Expert systems with applications · 2023Article
- An efficient hardware architecture based on an ensemble of deep learning models for COVID -19 prediction.Sustainable cities and society · 2022Article
- Machine learning-based approaches for identifying human blood cells harboring CRISPR-mediated fetal chromatin domain ablations.Scientific reports · 2022Article
- Detection and Prevention of Virus Infection.Advances in experimental medicine and biology · 2022Article
Corrections and comments
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Authors and funding
1 author at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Novel corona-virus (nCOV) has been declared as a pandemic that started from the city Wuhan of China. This deadly virus is infecting people rapidly and has targeted 4.93 million people across the world, with 227 K people being infected only in Italy. Cases of nCOV are quickly increasing whereas the number of nCOV test kits available in hospitals are limited. Under these conditions, an automated system for the classification of patients into nCOV positive and negative cases, is a much needed tool against the pandemic, helping in a selective use of the limited number of test kits. In this research, Convolutional Neural Network-based models (one block VGG, two block VGG, three block VGG, four block VGG, LetNet-5, AlexNet, and Resnet-50) have been employed for the detection of Corona-virus and SARS_MERS infected patients, distinguishing them from the healthy subjects, using lung X-ray scans, which has proven to be a challenging task, due to overlapping characteristics of different corona virus types. Furthermore, LSTM model has been used for time series forecasting of nCOV cases, in the following 10 days, in Italy. The evaluation results obtained, proved that the VGG1 model distinguishes the three classes at an accuracy of almost 91%, as compared to other models, whereas the approach based on the LSTM predicts the number of nCOV cases with 99% accuracy.
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Registered trials
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.