Classification of Echocardiogram Views using Deep Learning Models
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.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a copyright form (JACTA) that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).