Heart Chamber Segmentation in Cardiomegaly Conditions Using the CNN Method with U-Net Architecture

Abstract

Cardiomegaly is a disease in which sufferers show no symptoms and have symptoms such as shortness of breath, abnormal heartbeat and edema. Cardiomegaly will cause the sufferer's heart to pump harder than usual. Early diagnosis of cardiomegaly can help make decisions about whether the heart is abnormal or normal. In addition, due to the problem that manual examination takes time and requires human interpretation and experience, tools are needed to automatically develop and identify normal and abnormal hearts. Therefore, this study proposes cardiac chamber segmentation using 2D (two-dimensional) ultrasound convolutional neural networks for rapid cardiomegaly screening in clinical applications based on heart ultrasound examination. The proposed approach uses a CNN with a U-Net architecture model with abnormal and normal heart data. The research results obtained used the pixel matrix evaluation Avg_accuracy of 99.50%, Val_accuracy of 97.98% and Mean_IoU of 90.01%.

Keywords

• segmentation
• cardiomegaly
• convolutional neural network
• u-net
• deep learning