ArticleJournal of healthcare engineering2020
Exploiting Multiple Optimizers with Transfer Learning Techniques for the Identification of COVID-19 Patients.
Article in Journal of healthcare engineering, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- X-Ray Equipped with Artificial Intelligence: Changing the COVID-19 Diagnostic Paradigm during the Pandemic.BioMed research international · 2021Pooled it
- Optimization of vision transformer-based detection of lung diseases from chest X-ray images.BMC medical informatics and decision making · 2024Article
- Efficient novel network and index for alcoholism detection from EEGs.Health information science and systems · 2023Article
- LWSNet - a novel deep-learning architecture to segregate Covid-19 and pneumonia from x-ray imagery.Multimedia tools and applications · 2023Article
- Identification of normal and depression EEG signals in variational mode decomposition domain.Health information science and systems · 2022Article
- Early prediction of COVID-19 using ensemble of transfer learning.Computers & electrical engineering : an international journal · 2022Article
- Viral outbreaks detection and surveillance using wastewater-based epidemiology, viral air sampling, and machine learning techniques: A comprehensive review and outlook.The Science of the total environment · 2022Review
- Fast prototyping of a local fuzzy search system for decision support and retraining of hospital staff during pandemic.Health information science and systems · 2021Article
- Machine learning is the key to diagnose COVID-19: a proof-of-concept study.Scientific reports · 2021Article
- Multi-Block Color-Binarized Statistical Images for Single-Sample Face Recognition.Sensors (Basel, Switzerland) · 2021Article
- Exploiting Feature Selection and Neural Network Techniques for Identification of Focal and Nonfocal EEG Signals in TQWT Domain.Journal of healthcare engineering · 2021Article
- Structural Design and Preliminary Tests of a Novel Patient Transfer Apparatus for Medical Scenarios.Journal of healthcare engineering · 2021Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Due to the rapid spread of COVID-19 and its induced death worldwide, it is imperative to develop a reliable tool for the early detection of this disease. Chest X-ray is currently accepted to be one of the reliable means for such a detection purpose. However, most of the available methods utilize large training data, and there is a need for improvement in the detection accuracy due to the limited boundary segment of the acquired images for symptom identifications. In this study, a robust and efficient method based on transfer learning techniques is proposed to identify normal and COVID-19 patients by employing small training data. Transfer learning builds accurate models in a timesaving way. First, data augmentation was performed to help the network for memorization of image details. Next, five state-of-the-art transfer learning models, AlexNet, MobileNetv2, ShuffleNet, SqueezeNet, and Xception, with three optimizers, Adam, SGDM, and RMSProp, were implemented at various learning rates, 1e-4, 2e-4, 3e-4, and 4e-4, to reduce the probability of overfitting. All the experiments were performed on publicly available datasets with several analytical measurements attained after execution with a 10-fold cross-validation method. The results suggest that MobileNetv2 with Adam optimizer at a learning rate of 3e-4 provides an average accuracy, recall, precision, and
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