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

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).

Availability
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Central Library (REFERENSI) T406592020
T40659
Available but not for loan - Not for Loan
Detail Information
Series Title
-
Call Number
T406592020
Publisher
Inderalaya : Fakultas Ilmu Komputer, Universitas Sriwijaya.,
Collation
xvii,105 hlm.; ilus., tab.: 28 cm
Language
Indonesia
ISBN/ISSN
-
Classification
005.07
Content Type
-
Media Type
-
Carrier Type
-
Edition
-
Subject(s)
Ilmu Komputer
Convolutional Neural Networks
Specific Detail Info
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Statement of Responsibility
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