An Interpretable Object Detection-Based Model for the Diagnosis of Neonatal Lung Diseases Using Ultrasound Images

Lung Ultrasound (LUS) has been progressively used for the diagnosis of lung diseases in neonates due to its safety. Discussions with physicians revealed that a system that can detect specific lung features associated with neonatal lung diseases will be more useful than a simple image classification model. Therefore, a seven-class faster Region Proposal-based Convolutional Neural Network (fRCNN) as well as a RetinaNet were trained on lower posterior lung ultrasound videos to detect seven LUS features. Results show that fRCNN achieved a higher mean Average Precision (mAP) of 86.57% with an Intersection over Union (IoU) of 0.2 compared to RetinaNet with 61.15% mAP. A lung sliding feature detection method was proposed to differentiate between Pneumothorax and Normal scans. Using this method, we were able to classify 5 Pneumothorax (PTX) and 6 Normal video cases with 100% accuracy. We also developed a user-friendly GUI model that performs predictions on LUS video scans and outputs a video with the imposed LUS feature detections.