Article Per Year (5 Year)
p-Index From 2021 - 2026
2.832
P-Index
This Author published in this journals
All Journal International Journal of Electrical and Computer Engineering IAES International Journal of Artificial Intelligence (IJ-AI) International Journal of Advances in Applied Sciences ComEngApp : Computer Engineering and Applications Journal Bulletin of Electrical Engineering and Informatics Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) Sricommerce: Journal of Sriwijaya Community Services Sriwijaya Journal of Informatics and Applications
Darmawahyuni, Annisa
Unknown Affiliation
Arts Humanities Computer Science & IT Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Environmental Science Materials Science & Nanotechnology Mathematics Physics Social Sciences

Published : 21 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 21 Documents
Search

 1   2   3 
Fetal organ detection using feature enhancement with attention and residual block Bernolian, Nuswil; Nurmaini, Siti; Sapitri, Ade Iriani; Darmawahyuni, Annisa; Rachmatullah, Muhammad Naufal; Tutuko, Bambang; Firdaus, Firdaus
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 2: April 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i2.pp1593-1604

Abstract

The rapid advancements in fetal ultrasonography have significantly enhanced prenatal diagnosis in recent years. Deep learning (DL) architectures have further streamlined the process of organ detection, improved diagnostic accuracy, and reduced observer dependency. This study proposes a computer-aided DL approach for fetal organ segmentation using the you only look once (YOLO) algorithm, a state-of-the-art method for object detection and image segmentation. This study identified and classified 15 fetal organs, including the umbilical vein, stomach, abdomen, brain (trans-cerebellum, trans-thalamic, and trans-ventricular regions), femur, head, thorax (chest cavity), heart (circumference, left atrium, left ventricle, right atrium, right ventricle), and aorta. We compared the performance of YOLOv7, YOLOv8, YOLOv9, and YOLOv11 architectures. The results showed that YOLOv9 outperformed YOLOv7, YOLOv8, and YOLOv11 achieving mAP50 and mAP95 scores of 91.90% and 94.50%, respectively. This performance surpasses previous studies that focused on classifying only a limited number of fetal organs.
Co-Authors Al Ashri, Muhammad Rizky Alvi Syahrini Utami Ananda, Dea Agustria Archibald Hutahaean, Jerrel Adriel Armansyah, Risky Arum, Akhiar Wista Bambang Tutuko Bayu Wijaya Putra Dian Palupi Rini Dinda Lestarini Fahreza, Irvan Firdaus Firdaus Firdaus Gustriani, Gustriani Herman, Adelwin Isdwanta, Rendy Islami, Anggun Jordan Marcelino Kanda Januar Miraswan Khairunnisa, Cholidah Zuhroh Kurniawan, Anggy Tias Kurniawan, Anggy Tyas Maharani, Masayu Nadila Marcelino, Jordan Melliny, Vinny Dwi Meylani Utari Mira Afrina Muhammad Afif Muhammad Fachrurrozi Muhammad Naufal Rachmatullah Muhammad Naufal, Muhammad Muhammad Taufik Roseno, Muhammad Taufik Novi Yusliani Nuswil Bernolian PATIYUS AGUSTIANSYAH, PATIYUS Putri Mirani Rachmamtullah, Muhammad Naufal Rendy Isdwanta Rizal Sanif Rossi Passarella Samsuryadi Samsuryadi Sapitri, Ade Iriani Saputra, Tommy Sari, Ririn Purnama Sarifah Putri Raflesia, Sarifah Putri Sastradinata, Irawan Siti Nurmaini Suci Dwi Lestari Suhel Suhel Sutarno Sutarno Syaputra, Hadi Welly Nailis, Welly Winda Kurnia Sari Yesi Novaria Kunang Yuniarti, Emylia