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Full Scale Attention for Automated COVID-19 Diagnosis from CT Images.
Annu Int Conf IEEE Eng Med Biol Soc ; 2021: 3213-3216, 2021 11.
Article in English | MEDLINE | ID: covidwho-1566218
ABSTRACT
The wide spread of coronavirus pneumonia (COVID-19) has been a severe threat to global health since 2019. Apart from the nucleic acid detection, medical imaging examination is a vital diagnostic modality to confirm and treat the disease. Thus, implementing the automatic diagnosis of the COVID-19 bears particular significance. However, the limitations of data quality and size strongly hinder the clas-sification and segmentation performance and it also result in high misdiagnosis rate. To this end, we propose a novel full scale attention mechanism (FUSA) to capture more contextual dependencies of features, which enables the model easier to classify positive cases and improve the sensitivity. Specifically, FUSA parallelly extracts the information of channel domain and spatial domain, and fuses them together. The experimental study shows FUSA can significantly improve the COVID-19 automated diagnosis performance and eliminate false negative cases compared with other state-of-the-art ones.
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Full text: Available Collection: International databases Database: MEDLINE Main subject: Pneumonia / COVID-19 Type of study: Diagnostic study Limits: Humans Language: English Journal: Annu Int Conf IEEE Eng Med Biol Soc Year: 2021 Document Type: Article

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Full text: Available Collection: International databases Database: MEDLINE Main subject: Pneumonia / COVID-19 Type of study: Diagnostic study Limits: Humans Language: English Journal: Annu Int Conf IEEE Eng Med Biol Soc Year: 2021 Document Type: Article