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Ade Iriani Sapitri
Researcher at Sriwijaya University
Publications - 21
Citations - 108
Ade Iriani Sapitri is an academic researcher from Sriwijaya University. The author has contributed to research in topics: Computer science & Medicine. The author has an hindex of 2, co-authored 9 publications receiving 9 citations. Previous affiliations of Ade Iriani Sapitri include Telkom University.
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Journal ArticleDOI
AFibNet: an implementation of atrial fibrillation detection with convolutional neural network
Bambang Tutuko,Siti Nurmaini,Alexander Edo Tondas,Muhammad Naufal Rachmatullah,Annisa Darmawahyuni,Ria Esafri,Firdaus Firdaus,Ade Iriani Sapitri +7 more
TL;DR: These findings demonstrate that the proposed model approach can be used in a broad range of devices and validated to unknown data to derive feature maps and reliably detect the AF periods.
Journal ArticleDOI
Accurate Detection of Septal Defects With Fetal Ultrasonography Images Using Deep Learning-Based Multiclass Instance Segmentation
Siti Nurmaini,Muhammad Naufal Rachmatullah,Ade Iriani Sapitri,Annisa Darmawahyuni,Adithia Jovandy,Firdaus Firdaus,Bambang Tutuko,Rossi Passarella +7 more
TL;DR: The results suggest that the model used has a high potential to help cardiologists complete the initial screening for fetal congenital heart disease and a strong correlation between the predicted septal defects and ground truth as a mean average precision (mAP).
Journal ArticleDOI
Beat-to-Beat Electrocardiogram Waveform Classification Based on a Stacked Convolutional and Bidirectional Long Short-Term Memory
Siti Nurmaini,Annisa Darmawahyuni,Muhammad Naufal Rachmatullah,Jannes Effendi,Ade Iriani Sapitri,Firdaus Firdaus,Bambang Tutuko +6 more
TL;DR: In this paper, the authors proposed the delineation process by using bidirectional long short-term memory (BiLSTM) classifier, which is conducted as one beat to the next (beat-to-beat), that means the ECG waveform classification is start of P-wave1 to start of p-wave2.
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Deep Learning-Based Computer-Aided Fetal Echocardiography: Application to Heart Standard View Segmentation for Congenital Heart Defects Detection
Siti Nurmaini,Muhammad Naufal Rachmatullah,Ade Iriani Sapitri,Annisa Darmawahyuni,Bambang Tutuko,Firdaus Firdaus,Radiyati Umi Partan,Nuswil Bernolian +7 more
TL;DR: In this article, a deep learning-based computer-aided fetal heart echocardiography examinations with an instance segmentation approach was proposed, which inherently segments the four standard heart views and detects the defect simultaneously.
Journal ArticleDOI
Convolutional neural network for semantic segmentation of fetal echocardiography based on four-chamber view
Muhammad Naufal Rachmatullah,Siti Nurmaini,Ade Iriani Sapitri,Annisa Darmawahyuni,Bambang Tutuko,Firdaus Firdaus +5 more
TL;DR: A combination technique with U-Net and Otsu thresholding gives the best performances with 99.48%-pixel accuracy, 96.73% mean accuracy, 94.92% mean intersection over union, and 0.21% segmentation error in this paper.