Skripsi

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

---

Penilaian

---

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.

---

Availability

Inventory Code	Barcode	Call Number	Location	Status
2307003335	T121801	T1218012023	Central Library (Referens)	Available

Detail Information

Series Title

-

Call Number

T1218012023

Publisher

Indralaya : Jurusan Sistem Komputer, Fakultas Ilmu Komputer Universitas Sriwijaya., 2023

Collation

xv, 188 hlm.; Ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

-

Classification

005.110 7

Content Type

-

Media Type

-

Carrier Type

-

Edition

-

Subject(s)

Jurusan Sistem Komputer
Visual

Specific Detail Info

-

Statement of Responsibility

FIRA

Other version/related

File Attachment

Comments

---

You must be logged in to post a comment