Metric-Based Frame Selection and Deep Learning Model With Multi-Head Self Attention for Classification of Ultrasound Lung Video Images

Detection of COVID-19 manifestations in lung ultrasound (US) images has gained attention in recent times. The current state-of-the-art technique for distinguishing a healthy lung from COVID-19 infected or bacterial pneumonia infected lung uses non-adjacent frames or equally spaced frames from the video. However, the frame content or correlation between the selected frames has not been taken into consideration for frame selection. In this paper, a metric-based frame selection approach is proposed for three-way classification of lung US videos, and the influence of the frame selection method on image classification accuracy is studied. A deep learning model comprising of a pre-trained model (VGG16) for feature extraction, multi-head attention for feature calibration, global averaging for feature reduction, and a dense layer for classification is proposed. The pre-trained model is re-trained using cross-entropy loss with balanced weights to handle class imbalance. Two types of classification approaches are considered: i) few frames in a video are selected using the proposed metrics; and (ii) all frames in a video are considered. With VGG16 as the pre-trained model, a mean balanced sensitivity of COVID-19, bacterial pneumonia, and healthy classes with 0.82, 0.89, and 0.87, respectively was achieved using 5-fold cross-validation. The results show that even random selection of frames performs better than fixed frame selection and the proposed frame selection method outperforms the state-of-art fixed frame selection irrespective of the type of backbone model used for lung US classification.