Journal article  Open Access

Efficient lung ultrasound classification

Bruno A., Ignesti G., Salvetti O., Moroni D., Martinelli M.

Convolutional Neural Networks  EfficientNet  Lung Ultrasound  SARS-CoV-2  COVID-19  Pneumonia  Ensemble  Computer Vision  Supervised Learning  Deep Learning 

A machine learning method for classifying Lung UltraSound is here proposed to provide 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.

Source: Bioengineering (Basel) 10 (2023). doi:10.3390/bioengineering10050555

Publisher: MDPI AG, Basel, Svizzera


Back to previous page
BibTeX entry
	title = {Efficient lung ultrasound classification},
	author = {Bruno A. and Ignesti G. and Salvetti O. and Moroni D. and Martinelli M.},
	publisher = {MDPI AG, Basel, Svizzera},
	doi = {10.3390/bioengineering10050555},
	journal = {Bioengineering (Basel)},
	volume = {10},
	year = {2023}