Background:
Valvular heart disease is a serious disease leading to mortality and increasing
medical care cost. The aortic valve is the most common valve affected by this disease.
Doctors rely on echocardiogram for diagnosing and evaluating valvular heart disease. However, the
images from echocardiogram are poor in comparison to Computerized Tomography and Magnetic
Resonance Imaging scan. This study proposes the development of Convolutional Neural Networks
(CNN) that can function optimally during a live echocardiographic examination for detection of the
aortic valve. An automated detection system in an echocardiogram will improve the accuracy of
medical diagnosis and can provide further medical analysis from the resulting detection.
Methods:
Two detection architectures, Single Shot Multibox Detector (SSD) and Faster Regional
based Convolutional Neural Network (R-CNN) with various feature extractors were trained on
echocardiography images from 33 patients. Thereafter, the models were tested on 10 echocardiography
videos.
Results:
Faster R-CNN Inception v2 had shown the highest accuracy (98.6%) followed closely by
SSD Mobilenet v2. In terms of speed, SSD Mobilenet v2 resulted in a loss of 46.81% in framesper-
second (fps) during real-time detection but managed to perform better than the other neural
network models. Additionally, SSD Mobilenet v2 used the least amount of Graphic Processing
Unit (GPU) but the Central Processing Unit (CPU) usage was relatively similar throughout all
models.
Conclusion:
Our findings provide a foundation for implementing a convolutional detection system
to echocardiography for medical purposes.
Dynamic Training of a Novelty Classifier Algorithm for Real-Time Early Seizure Onset Detection
