KLASIFIKASI DAN VISUALISASI ENAM KELAS ABNORMALITAS JANTUNG JANIN MENGGUNAKAN CONVOLUTIONAL NEURAL NETWpORK DAN GUIDED BACKPROPAGATION

HIDAYATULLAH, DEWA PURNAMA and Nurmaini, Siti (2023) KLASIFIKASI DAN VISUALISASI ENAM KELAS ABNORMALITAS JANTUNG JANIN MENGGUNAKAN CONVOLUTIONAL NEURAL NETWpORK DAN GUIDED BACKPROPAGATION. Undergraduate thesis, Sriwijaya University.

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Abstract

This study presents and analyzes deep learning techniques to classify abnormalities in fetal heart images. This research compares four convolutional neural network (CNN) architectures to choose the best architecture with satisfactory results, and performs visualization using the guided backpropagation method to provide insight regarding the part of the image that plays a role in the classification process. Xception architecture has the best classification performance with accuracy, sensitivity and specifications on validation data were 100%, 100%, and 100%, respectively and 90.2%, 65.7%, and 94.2% on unseen data, respectively. The proposed model yields satisfactory results, which means this model can support fetal cardiologists to interpret decisions to improve diagnostic abnormalities on fetal heart images.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Convolutional Neural Networks (CNN), Guided Backpropagation, Classification, Citra Jantung Janing, Abnormalitas.
Subjects:	Q Science > Q Science (General) > Q1-390 Science (General) > Q223.M517 Science -- Information services. Information storage and retrieval systems --Science.
Divisions:	09-Faculty of Computer Science > 56201-Computer Systems (S1)
Depositing User:	Dewa Purnama Hidayatullah
Date Deposited:	27 Jul 2023 07:38
Last Modified:	27 Jul 2023 07:38
URI:	http://repository.unsri.ac.id/id/eprint/121801

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