Classification of Echocardiogram Views using Deep Learning Models

  • Lee Qi Zian Univerisiti Teknikal Malaysia Melaka
  • Yogan Jaya Kumar Univerisiti Teknikal Malaysia Melaka
  • Goh Ong Sing Univerisiti Teknikal Malaysia Melaka
  • Zi Ye Wenzhou Polytechnic, China


Cardiovascular disease has always been one of the main causes of death in the world. One of the way to diagnose cardiovascular disease is by using echocardiography. However, this method of diagnosis requires cardiovascular knowledge and sometimes it can be very hard to recognize the views of echocardiogram without expertise in that particular field. The main purpose of this study is to develop and compare deep learning models to classify the views of echocardiogram. VGG16, VGG19, InceptionV3 and MobileNet are used to develop the model to classify the echocardiogram view. After training, the models are evaluated by using classification measures, confusion matrix and confidence test. From the experimental findings, the VGG16 model obtained the best result on both F1 score and accuracy. However, for the confidence score test, MobileNet model achieved better results.

How to Cite
Qi Zian, L., Jaya Kumar, Y., Ong Sing, G., & Ye, Z. (2020). Classification of Echocardiogram Views using Deep Learning Models. Journal of Advanced Computing Technology and Application (JACTA), 2(2), 33-40. Retrieved from