COVID-19 detection from lung ultrasound images
Por:
Mateu M., Olveres J., Escalante-Ramírez B.
Publicada:
1 ene 2022
Resumen:
Early-stage detection of Coronavirus Disease 2019 (COVID-19) is crucial for patient medical attention. Since lungs are the most affected organs, monitoring them constantly is an effective way to observe sickness evolution. The most common technique for lung-imaging and evaluation is Computed Tomography (CT). However, its costs and effects over human health has made Lung Ultrasound (LUS) a good alternative. LUS does not expose the patient to radiation and minimizes the risk of contamination. Also, there is evidence of a relation between different artifacts on LUS and lung’s diseases coming from the pleura, whose abnormalities are related with most acute respiratory disorders. However, LUS often requires an expert clinical interpretation that may increase diagnosis time or decrease diagnosis performance. This paper describes and compares machine learning classification methods namely Naive Bayes (NB) Support Vector Machine (SVM), K-Nearest Neighbor (K-NN) and Random Forest (RF) over several LUS images. They obtain a classification between lung images with COVID-19, pneumonia, and healthy patients, using image’s features previously extracted from Gray Level Co-Occurrence Matrix (GLCM) and histogram’s statistics. Furthermore, this paper compares the above classic methods with different Convolutional Neural Networks (CNN) that classifies the images in order to identify these lung’s diseases. © 2022 SPIE.
Filiaciones:
Mateu M.:
Posgrado en Ingeniería, Universidad Nacional Autonoma de Mexico, Mexico City, Mexico
Olveres J.:
Facultad de Ingenería, Universidad Nacional Autónoma de México, Mexico City, Mexico
Centro de Estudios en Computación Avanzada, Universidad Nacional Autónoma de México, Mexico City, Mexico
Escalante-Ramírez B.:
Facultad de Ingenería, Universidad Nacional Autónoma de México, Mexico City, Mexico
Centro de Estudios en Computación Avanzada, Universidad Nacional Autónoma de México, Mexico City, Mexico
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