Annisa Darmawahyuni
Orcid: 0000-0002-0229-5717
According to our database1,
Annisa Darmawahyuni
authored at least 17 papers
between 2019 and 2024.
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Bibliography
2024
A Real-Time End-to-End Framework with a Stacked Model Using Ultrasound Video for Cardiac Septal Defect Decision-Making.
J. Imaging, 2024
IEEE Access, 2024
2023
Improved delineation model of a standard 12-lead electrocardiogram based on a deep learning algorithm.
BMC Medical Informatics Decis. Mak., December, 2023
Accurate Fetal QRS-Complex Classification from Abdominal Electrocardiogram Using Deep Learning.
Int. J. Comput. Intell. Syst., December, 2023
Automatic echocardiographic anomalies interpretation using a stacked residual-dense network model.
BMC Bioinform., December, 2023
Medical Biol. Eng. Comput., September, 2023
Empowering AI-Diagnosis: Deep Learning Abilities for Accurate Atrial Fibrillation Classification.
Int. J. Online Biomed. Eng., 2023
2022
Short Single-Lead ECG Signal Delineation-Based Deep Learning: Implementation in Automatic Atrial Fibrillation Identification.
Sensors, 2022
Automated Precancerous Lesion Screening Using an Instance Segmentation Technique for Improving Accuracy.
Sensors, 2022
Deep learning-based electrocardiogram rhythm and beat features for heart abnormality classification.
PeerJ Comput. Sci., 2022
An improved semantic segmentation with region proposal network for cardiac defect interpretation.
Neural Comput. Appl., 2022
2021
Deep Learning-Based Computer-Aided Fetal Echocardiography: Application to Heart Standard View Segmentation for Congenital Heart Defects Detection.
Sensors, 2021
AFibNet: an implementation of atrial fibrillation detection with convolutional neural network.
BMC Medical Informatics Decis. Mak., 2021
Beat-to-Beat Electrocardiogram Waveform Classification Based on a Stacked Convolutional and Bidirectional Long Short-Term Memory.
IEEE Access, 2021
2020
Robust detection of atrial fibrillation from short-term electrocardiogram using convolutional neural networks.
Future Gener. Comput. Syst., 2020
Accurate Detection of Septal Defects With Fetal Ultrasonography Images Using Deep Learning-Based Multiclass Instance Segmentation.
IEEE Access, 2020
2019
Deep Learning with a Recurrent Network Structure in the Sequence Modeling of Imbalanced Data for ECG-Rhythm Classifier.
Algorithms, 2019