KLASIFIKASI ABNORMALITAS STRUKTUR JANTUNG ANAK DAN VISUALISASI MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK DAN GUIDED BACKPROPAGATION

SALAM, BAYU IZZAH and S, Nurmaini (2024) KLASIFIKASI ABNORMALITAS STRUKTUR JANTUNG ANAK DAN VISUALISASI MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK DAN GUIDED BACKPROPAGATION. Undergraduate thesis, Sriwijaya University.

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Abstract

The application of artificial intelligence in these days is so much, it is beginning to enter a wide range of fields in the world, one of which is in the field of biomedicine. CNN uses several layers to help the classification process, especially the one on this study of the child's heart. The child's heart image will be processed into a previously created model that aims to recognize each class on each image, which will be subsequently grouped into four classes, namely atrial septal defect (ASD), atrioventricular septaldefect (AVSD), ventricultural septal Defect (VSD), and NORMAL. The models used are ResNet50, MobileNetV2, XceptionNet, and DenseNet121, where Xception achieved the best results at the validation and unseen test stages, with accuracy of 99% and 76%. After the classification process is completed, the next stage is the visualization process using Guided Backpropagation (Guided BP). Guided BP aims to clarify the parts on the child's heart image in order to mark any part that has the largest percentage in the process of classification. At this stage of visualization, the DenseNet121 model has a good result when compared to the other three models.

Item Type:	Thesis (Undergraduate)
Uncontrolled Keywords:	Convolutional Neural Network, GUided Backpropagation
Subjects:	T Technology > T Technology (General) > T1-995 Technology (General)
Divisions:	09-Faculty of Computer Science > 56201-Computer Systems (S1)
Depositing User:	Bayu Izzah Salam
Date Deposited:	23 Jan 2024 02:00
Last Modified:	23 Jan 2024 02:00
URI:	http://repository.unsri.ac.id/id/eprint/139284

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