Detection of Pulmonary Embolism: Workflow Architecture and Comparative Analysis of the CNN Models

Abstract

Machine learning has proven to be a practical medical image processing technique for pattern discovery in low-quality labelled and unlabeled datasets. Deep vein thrombosis and pulmonary embolism are both examples of venous thromboembolism, which is a key factor in patient mortality and necessitates prompt diagnosis by experts. An immediate diagnosis and course of treatment are necessary for the life-threatening cardiovascular condition known as pulmonary embolism (PE). In the study of medical imaging, especially the identification of PE, machine learning (ML) algorithms have produced encouraging results. This study's objective is to assess how well machine learning (ML) algorithms perform in identifying PE in computed tomography (CT) scans. A range of ML approaches were used to the dataset, including deep learning algorithms such as convolutional neural networks. The effectiveness of PE detection systems can be greatly enhanced by the use of cutting-edge methodologies like deep learning, which lowers the possibility of incorrect diagnoses and enables the quick administration of therapy to individuals who require it. This work contributes to the growing body of evidence that supports the use of ML in medical imaging and diagnosis. Future research should examine how these algorithms might be included into clinical workflows, resolving any potential implementation challenges, and making sure their adoption is done so in a secure and efficient way. In this study, we provide a thorough evaluation of three different models: the streamlined architecture MobileNetV2 with an accuracy of 96%, compared to other models like the Xception model with an accuracy of 91%, and the Efficientnet B5 model with an accuracy of 97%, after observation and process following.