Skripsi

KLASIFIKASI DAN VISUALISASI ENAM KELAS ABNORMALITAS JANTUNG JANIN MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORK DAN SALIENCY MAP

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Penilaian

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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 Saliency Map method to provide insight regarding the part of the image that plays a role in the classification process. DenseNet121 architecture has the best classification performance with accuracy, sensitivity and specifications on validation data were 100%, 100%, and 100%, respectively and MobileNetV2 has the best classification performance with accuracy, sensitivity and specifications with score 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.

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2307003806	T126021	T1260212023	Central Library (REFERENS)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T1260212023

Publisher

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

Collation

xvi, 114 hlm.; ilus.; 29 cm

Language

Indonesia

ISBN/ISSN

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Classification

004.21 07

Content Type

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Media Type

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Carrier Type

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Edition

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Subject(s)

Jurusan Sistem Komputer
Sistem Analis dan Desain Komputer

Specific Detail Info

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Statement of Responsibility

ANUG

Other version/related

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