PENGENALAN POLA PANDANGAN JANTUNG JANIN MENGGUNAKAN ARSITEKTUR FASTER R-CNN

PRATAMA. R, M. ADHITYA RESKI and Nurmaini, Siti (2021) PENGENALAN POLA PANDANGAN JANTUNG JANIN MENGGUNAKAN ARSITEKTUR FASTER R-CNN. Undergraduate thesis, Sriwijaya University.

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Abstract

Clinicians need the capabilities of deep learning to minimize healthcare costs, diagnose patient diseases, increase the chance to recover, and save many lifes. Deep learning has also proven to optimize the accuracy of diagnoses. The literatures of fetal heart are still limited due to heart screening views data can't be easily access by everyone. This study has explored four fetal heart screening views by ultrasongraphy videos. It consists of four chamber view, left ventricular outflow tract, right ventricular outflow tract, and three vessel and thrachea view. For pre-processing data, the videos have converted to frames. Also, the frames have annotated and augmented in training data. Faster R-CNN have implemented to get more accuracy and better optimization. This study used VGG16 and ResNet50 as backbone or feature extractors and pre-trained model from PASCAL VOC 2007. As the results, VGG16 outperformed ResNet50 model. Faster R-CNN with VGG16 achieves AP (average precison) for four chamber view 86.09%, left ventricular outflow tract 86.87%, right ventricular outflow tract 96.35%, and three vessel and trachea view 89.46%. For unseen data, the model gets mAP (mean average precision) 89.69%.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Pattern Recognition, Object Detection
Subjects:	Q Science > Q Science (General) > Q334-342 Computer science. Artificial intelligence. Algorithms. Robotics. Automation.
Divisions:	09-Faculty of Computer Science > 56201-Computer Systems (S1)
Depositing User:	M. Adhitya Reski Pratama. R
Date Deposited:	03 Aug 2021 03:24
Last Modified:	03 Aug 2021 03:24
URI:	http://repository.unsri.ac.id/id/eprint/51046

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