Detecting Pulmonary Embolism using Deep Learning Algorithms

Nowadays, pulmonary vascular disorders, which might result in pulmonary emboli or pulmonary hypertension, affect majority of patients. To diagnose alterations in vascular trees, a manual and automatic study of the ill person's chest CT imaging is performed. The manual analysis of CTPA scans is time-consuming, non-standardized, and exhausting. Therefore, semi-automatic and automatic vascular tree separation in CTPA scans is increasingly used, which enables medical professionals to precisely identify aberrant conditions. Different techniques for pulmonary vascular disease identification and classification using deep learning and machine learning methods have been carried out recently. Here we are using deep learning algorithms like Resnet50,Densenet121 and VGG19 for automatic classification of pulmonary vessels for detecting pulmonary diseases with increased accuracy.