Deep learning-based uncertainty estimation for accurate lung ultrasound image analysis

Abstract

Lung ultrasound imaging has become an important diagnostic tool for various respiratory conditions. Deep learning models have shown impressive results in classifying abnormalities in lung ultrasound images. However, these models typically provide deterministic predictions, disregarding the inherent uncertainty inherent in medical image analysis. This research paper introduces a novel approach to quantify uncertainty in deep learning models for accurate lung ultrasound image analysis. The proposed framework leverages a unique combination of Monte Carlo Dropout and Bayesian neural networks to provide reliable uncertainty estimates. By integrating these techniques, the model gains the ability to capture and represent the inherent uncertainty associated with medical image analysis. Extensive experiments conducted on a diverse dataset demonstrate the effectiveness and novelty of this approach. The inclusion of uncertainty estimation enhances classification accuracy and decision-making processes in lung ultrasound-based diagnosis, setting a new standard for the application of deep learning in medical image analysis. The novel methodology presented in this study has the potential to foster greater trust in AI-based diagnostic tools, promoting their integration into clinical practice and ultimately improving patient care and outcomes.