ArticleComputers in biology and medicine2021
A comparative analysis of eleven neural networks architectures for small datasets of lung images of COVID-19 patients toward improved clinical decisions.
Article in Computers in biology and medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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24 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Automated COVID-19 diagnosis and prognosis with medical imaging and who is publishing: a systematic review.Physical and engineering sciences in medicine · 2022Pooled it
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- Healthcare As a Service (HAAS): CNN-based cloud computing model for ubiquitous access to lung cancer diagnosis.Heliyon · 2023Article
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8 authors.
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Abstract
The 2019 novel severe acute respiratory syndrome coronavirus 2-SARS-CoV2, commonly known as COVID-19, is a highly infectious disease that has endangered the health of many people around the world. COVID-19, which infects the lungs, is often diagnosed and managed using X-ray or computed tomography (CT) images. For such images, rapid and accurate classification and diagnosis can be performed using deep learning methods that are trained using existing neural network models. However, at present, there is no standardized method or uniform evaluation metric for image classification, which makes it difficult to compare the strengths and weaknesses of different neural network models. This paper used eleven well-known convolutional neural networks, including VGG-16, ResNet-18, ResNet-50, DenseNet-121, DenseNet-169, Inception-v3, Inception-v4, SqueezeNet, MobileNet, ShuffeNet, and EfficientNet-b0, to classify and distinguish COVID-19 and non-COVID-19 lung images. These eleven models were applied to different batch sizes and epoch cases, and their overall performance was compared and discussed. The results of this study can provide decision support in guiding research on processing and analyzing small medical datasets to understand which model choices can yield better outcomes in lung image classification, diagnosis, disease management and patient care.
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