Certainty-Based Deep Fused Neural Network Using Transfer Learning and Adaptive Movement Estimation for the Diagnosis of Cardiomegaly

Abstract

Cardiomegaly is a radiographic abnormality, and it has significant prognosis importance in the population. Chest X-ray images can identify it. Early detection of cardiomegaly reduces the risk of congestive heart failure and systolic dysfunction. Due to the lack of radiologists, there is a demand for the artificial intelligence tool for the early detection of cardiomegaly. The cardiomegaly X-ray dataset is extracted from the cheXpert database. Totally, 46195 X-ray records with a different view such as AP view, PA views, and lateral views are used to train and validate the proposed model. The artificial intelligence app named CardioXpert is constructed based on deep neural network. The transfer learning approach is adopted to increase the prediction metrics, and an optimized training method called adaptive movement estimation is used. Three different transfer learning-based deep neural networks named APNET, PANET, and LateralNET are constructed for each view of X-ray images. Finally, certainty-based fusion is performed to enrich the prediction accuracy, and it is named CardioXpert. As the proposed method is based on the largest cardiomegaly dataset, hold-out validation is performed to verify the prediction accuracy of the proposed model. An unseen dataset validates the model. These deep neural networks, APNET, PANET, and LateralNET, are individually validated, and then the fused network CardioXpert is validated. The proposed model CardioXpert provides an accuracy of 93.6%, which is the highest at this time for this dataset. It also yields the highest sensitivity of 94.7% and a precision of 97.7%. These prediction metrics prove that the proposed model outperforms all the state-of-the-art deep transfer learning methods for diagnosing cardiomegaly thoracic disorder. The proposed deep learning neural network model is deployed as the web app. The cardiologist can use this prognostic app to predict cardiomegaly disease faster and more robust in the early state by using low-cost and chest X-ray images.