Semantic Segmentation and Pathology Localization in Lung Ultrasound Images Using Transfer Learning

Significant progress has been made to leverage machine learning towards decision support diagnosis from ultrasound in medical imaging. Due to the continuous challenges associated with evolving characteristics of COVID-19, the availability of fast, safe, and highly sensitive diagnostic tools is imperative. Where ultrasound imaging has been proven superior to X-rays and CT scans, the main limitation to its use is operator dependency and experience. In this study, we propose a novel transfer learning and semantic segmentation framework for automatic detection and localization of multiple lung pathologies in Lung Ultrasound images. The proposed framework allows better interpretation and explanation of the model decisions, with clear visualization and localization of the different pathologies. The experimental evaluation of the proposed framework was done on an open-source Lung ultrasound imaging dataset which is labeled and annotated by team of radiologists. The proposed approach was validated using other benchmarks models for comparison in terms of DICE coefficients at 0.98 and IOU Score at 0.97 which outperforms the other benchmark models quite significantly. This provides interpretation of the reasoning behind the decision made by the model leading to higher rates of acceptance with clinicians.