Article
Version 1
Preserved in Portico This version is not peer-reviewed
Efficient Lung Ultrasound Classification
†
All the authors contributed equally to this work.
Version 1
: Received: 11 March 2023 / Approved: 13 March 2023 / Online: 13 March 2023 (02:41:13 CET)
A peer-reviewed article of this Preprint also exists.
Bruno, A.; Ignesti, G.; Salvetti, O.; Moroni, D.; Martinelli, M. Efficient Lung Ultrasound Classification. Bioengineering 2023, 10, 555. Bruno, A.; Ignesti, G.; Salvetti, O.; Moroni, D.; Martinelli, M. Efficient Lung Ultrasound Classification. Bioengineering 2023, 10, 555.
Abstract
A machine learning method for classifying Lung UltraSound is here proposed to pro-
vide a point of care tool for supporting a safe, fast and accurate diagnosis, that can also be useful during a pandemic like as SARS-CoV-2. Given the advantages (e.g. safety, rapidity, portability, cost-effectiveness) provided by the ultrasound technology over other methods (e.g. X-ray, computer tomography, magnetic resonance imaging), our method was validated on the largest LUS public dataset. Focusing on both accuracy and efficiency, our solution is based on an efficient adaptive ensembling of two EfficientNet-b0 models reaching 100% of accuracy, which, to our knowledge, outperforms the previous state-of-the-art. The complexity of this solution keeps the number of parameters in the same order as an EfficientNet-b0 by adopting specific design choices that are adaptive ensembling with a combination layer, ensembling performed on the deep features, minimal ensemble only two weak models. Moreover, a visual analysis of the saliency maps on sample images of all the classes of the dataset reveals where the focus is on an inaccurate weak model versus an accurate model.
Keywords
Convolutional Neural Networks; EfficientNet; Lung Ultrasound; SARS-CoV-2; COVID-19; Pneumonia; Ensemble; Computer Vision; Supervised Learning; Deep Learning
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Comments (0)
We encourage comments and feedback from a broad range of readers. See criteria for comments and our Diversity statement.
Leave a public commentSend a private comment to the author(s)
* All users must log in before leaving a comment
