Skripsi

DETEKSI RUANG JANTUNG ANAK PADA PANDANGAN 4 CHAMBER MENGGUNAKAN ARSITEKTUR FASTER R-CNN

---

Penilaian

---

The heart is a vital human organ that sometimes has dangerous abnormal conditions, such as: atrioventricular septal defect (AVSD), atrial septal defect (ASD), and ventricular septal defect (VSD). In this final project, we propose a Faster R-CNN architecture method using several backbones such as VGG16, Resnet50 and mobilenetv1 as heart detectors. The dataset used is a dataset in the form of an abnormal child's heart frame with a 4-chamber point of view. This research focuses on the level of accuracy generated from the data frame to build a Faster R-CNN model that is effective in detecting abnormal heart chambers in children such as the right atrium, left atrium, right ventricle, left ventricle and holes. Parameter assessment using mean average precision (mAP) is a benchmark to determine the level of success of the method in detecting objects, especially in abnormal children's heart. The best results were obtained in the model using the VGG16 backbone with a learning rate of 0.001 with an average mAP value of 92.32% and in the unseen data the best results were obtained in the model using the VGG16 backbone learning rate of 0.001 with an average mAP value of 71.49%.

---

Availability

Inventory Code	Barcode	Call Number	Location	Status
2107002435	T53886	T538862021	Central Library (Referens)	Available but not for loan - Not for Loan

Detail Information

Series Title

-

Call Number

T538862021

Publisher

Inderalaya : Prodi Sistem Komputer, Fakultas Ilmu Komputer Uniersitas Sriwijaya., 2021

Collation

xiii, 42 hlm,: ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

-

Classification

005.707

Content Type

Text

Media Type

-

Carrier Type

-

Edition

-

Subject(s)

Prodi Sistem Komputer
Data Sistem Komputer

Specific Detail Info

-

Statement of Responsibility

MURZ

Other version/related

File Attachment

Comments

---

You must be logged in to post a comment