Skripsi

DETEKSI DEFECT SEPTUM JANTUNG JANIN BERBASIS CITRA 2 DIMENSI MENGGUNAKAN CONVOLUTIONAL NEURAL NETWORKS

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Penilaian

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Congenital heart disease is a common disease that can be life-threatening. CHD has an important role in knowing the early diagnosis of the heart, especially the fetus. Medical image analysis is one of the topics that can support the diagnosis process, especially the occurrence of septal defects. The image analysis process can be done by segmenting, detecting, and classifying it. This is the main key in carrying out the analysis process in diagnosing diseases of defects. Convolutional neural network (CNN) is a deep learning technique that is often used, especially in image analysis. RCNN mask is a CNN architecture that can perform segmentation, detection, and classification processes simultaneously called instance segmentation (Johnson, 2018). The proposed approach uses CNN with the RCNN Mask architecture using fetal cardiac septal defect data. The results showed that the model performance obtained was 97.46% mean Average Precision (mAP), 77.49% Intersection over Union (IoU), and 87.22% Dice Score Similarity (DSC).

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Availability

Inventory Code	Barcode	Call Number	Location	Status
2007000912	T40659	T406592020	Central Library (REFERENSI)	Available but not for loan - Not for Loan

Detail Information

Series Title

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Call Number

T406592020

Publisher

Inderalaya : Fakultas Ilmu Komputer, Universitas Sriwijaya., 2020

Collation

xvii,105 hlm.; ilus., tab.: 28 cm

Language

Indonesia

ISBN/ISSN

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Classification

005.07

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)

Ilmu Komputer
Convolutional Neural Networks

Specific Detail Info

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Statement of Responsibility

EM

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