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All Journal The Indonesian Journal of Computer Science
Rohmah, Jauharil
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Computer Science & IT Electrical & Electronics Engineering Engineering

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Fully Convolutional Neural Network untuk Segmentasi Diagnosis Jantung pada Ekokardiografi Short Axis Rohmah, Jauharil
The Indonesian Journal of Computer Science Vol. 13 No. 5 (2024): The Indonesian Journal of Computer Science (IJCS)
Publisher : AI Society & STMIK Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33022/ijcs.v13i5.4434

Abstract

This research discusses the application of Fully Convolutional Neural Networks (FCNs) for echocardiographic image segmentation as a diagnostic aid for heart conditions. Image segmentation is crucial in identifying cardiac structures, enabling accurate and timely diagnosis. In this study, the FCN-8 architecture is employed to perform segmentation on echocardiographic images with a short-axis view. The segmentation results are evaluated using several metrics, including Dice Coefficient, Intersection over Union (IoU), precision, and recall. Based on the evaluation, the model demonstrated good performance in distinguishing cardiac structures, with a Dice Coefficient of 0.85 and IoU of 0.79. This approach shows potential in assisting physicians in making faster and more accurate diagnoses for patients with heart disorders. This study concludes that FCN-8 can be an effective tool in supporting the diagnostic process in the medical field
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