PERBANDINGAN KINERJA KLASIFIKASI ABNORMALITAS JANTUNG JANIN DENGAN MENGGUNAKAN 10 STRUKTUR CONVOLUTIONAL NEURAL NETWORK

FAHRI, MUHAMMAD FAZRIL and ‪Siti, ‪Nurmaini (2023) PERBANDINGAN KINERJA KLASIFIKASI ABNORMALITAS JANTUNG JANIN DENGAN MENGGUNAKAN 10 STRUKTUR CONVOLUTIONAL NEURAL NETWORK. Undergraduate thesis, Sriwijaya University.

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Abstract

The use of Artificial Intelligence Technology in the field of Computer Vision has enabled the classification of fetal heart abnormalities by utilizing one of the Classification approaches. With technological advances, the image classification process can be implemented using Deep Learning (DL) models. This research uses the architecture of 10 Convolutional Neural Network Architectures for the Fetal Heart Abnormality Classification process. There are 10 Convolutional Neural Network Architectures namely Densenet121, Densenet169, Densenet201, InceptionV3, Resnet50, Resnet101, Resnet152, VGG16, VGG19, and Xception. The best Unseen Test performance is achieved by the Resnet101 model. Unseen performance results on Accuracy, Sensitivity, and Specificity evaluation metrics averaged 87.7%, 57% and 93.8%.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Kecerdasan Buatan, Visi Komputer, Klasifikasi, Citra Medis jantung janin, Deep Learning
Subjects:	T Technology > TK Electrical engineering. Electronics Nuclear engineering > TK7885-7895 Computer engineering. Computer hardware
Divisions:	09-Faculty of Computer Science > 56201-Computer Systems (S1)
Depositing User:	Muhammad Fazril Fahri
Date Deposited:	04 Aug 2023 08:32
Last Modified:	04 Aug 2023 08:32
URI:	http://repository.unsri.ac.id/id/eprint/125586

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