Efficient Lung Ultrasound Classification

Abstract

A machine learning method for classifying lung ultrasound is proposed here to provide a point of care tool for supporting a safe, fast, and accurate diagnosis that can also be useful during a pandemic such as SARS-CoV-2. Given the advantages (e.g., safety, speed, portability, cost-effectiveness) provided by the ultrasound technology over other examinations (e.g., X-ray, computer tomography, magnetic resonance imaging), our method was validated on the largest public lung ultrasound 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 models by at least 5%. The complexity is restrained by adopting specific design choices: ensembling with an adaptive combination layer, ensembling performed on the deep features, and minimal ensemble using two weak models only. In this way, the number of parameters has the same order of magnitude of a single EfficientNet-b0 and the computational cost (FLOPs) is reduced at least by 20%, doubled by parallelization. Moreover, a visual analysis of the saliency maps on sample images of all the classes of the dataset reveals where an inaccurate weak model focuses its attention versus an accurate one.

Efficient Deep Learning Approach for Olive Disease Classification

“…Data is organized best-to-worst fold (top-to-bottom), and then the models corresponding to the first two rows in the left table are used as weak models for the ensemble. This approach to ensembling has been recently introduced and discussed in [21], [22]; it has already proved excellent applicability to AI-based methods for agriculture [23].…”

“…Another real-world application using this solution on a different domain was presented and discussed in [22]: using a public database of lung ultrasound, the SOTA was reached with 100% of accuracy in classifying healthy from Covid-19 from pneumonia cases.…”