Garuda - Garba Rujukan Digital

Heart Chamber Segmentation in Cardiomegaly Conditions Using the CNN Method with U-Net Architecture Saputra, Tommy; Nurmaini, Siti; Roseno, Muhammad Taufik; Syaputra, Hadi
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol 12, No 3 (2023): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v12i3.1976

Cardiomegaly is a disease in which sufferers show no symptoms and have symptoms such as shortness of breath, abnormal heartbeat and edema. Cardiomegaly will cause the sufferer's heart to pump harder than usual. Early diagnosis of cardiomegaly can help make decisions about whether the heart is abnormal or normal. In addition, due to the problem that manual examination takes time and requires human interpretation and experience, tools are needed to automatically develop and identify normal and abnormal hearts. Therefore, this study proposes cardiac chamber segmentation using 2D (two-dimensional) ultrasound convolutional neural networks for rapid cardiomegaly screening in clinical applications based on heart ultrasound examination. The proposed approach uses a CNN with a U-Net architecture model with abnormal and normal heart data. The research results obtained used the pixel matrix evaluation Avg_accuracy of 99.50%, Val_accuracy of 97.98% and Mean_IoU of 90.01%.

Dilatasi Inkremental Menggunakan Metode CNN Untuk Klasifikasi Tumor Otak Dengan Arsitektur VCG16 dan Resnet50 Saputra, Tommy; Roseno, Muhammad Taufik; Syaputra, Hadi
Generic Vol 16 No 2 (2024): Vol 16, No 2 (2024)
Publisher : Fakultas Ilmu Komputer, Universitas Sriwijaya

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18495/generic.v16i2.190

Klasifikasi tumor otak adalah tugas yang menantang di bidang pemrosesan citra medis. Teknologi kini telah memungkinkan dokter medis untuk memiliki bantuan tambahan untuk diagnosis. Penelitian ini bertujuan untuk mengklasifikasikan tumor otak menggunakan gambar MRI, yang dikumpulkan dari pasien anonim dan simulator otak buatan. Baru-baru ini, teknik berbantuan komputer seperti menggunakan deep learning sebagai ekstraksi fitur, dan teknik klasifikasi digunakan secara intensif untuk mendiagnosis otak pasien untuk memeriksa apakah ada tumor. Dalam penelitian ini diusulkan model klasifikasi tumor otak menggunakan Convolutional Neural Network yang dapat menklasifikasikan tumor otak secara akurat. Data yang digunakan berupa data MRI tumor otak sebanyak 253 data tumor otak. Dataset yang dugankan dibagi menjadi data pelatihan dan pengujian. Penelitian menghasilkan model klasifikasi tumor otak dengan menggunakan arsitektur VCG16 dan Resnet50. Model menghasilkan nilai rata-rata akurasi sebesar 80%, Recall 85% dan Presisi 70%. Penelitian menunjukkan kinerja Resnet50 menunjukkan kemampuan model untuk mengklasifikasikan tumor otak secara akurat.

Residual pixel-wise semantic segmentation for assessing enlarged fetal heart: a preliminary study Roseno, Muhammad Taufik; Nurmaini, Siti; Rini, Dian Palupi; Saputra, Tommy; Mirani, Putri; Rachmatullah, Muhammad Naufal; Darmawahyuni, Annisa; Sapitri, Ade Iriani; Syaputra, Hadi
Bulletin of Electrical Engineering and Informatics Vol 14, No 4: August 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i4.9244

The four-chamber view is a crucial scan plane routinely employed in both second-trimester perinatal screening and fetal echocardiographic examinations. Sonographers typically measure biometrics in this plane, such as the cardiothoracic ratio (CTR) and heart axis, to diagnose fetal heart anomalies. However, due to the echocardiographic artifacts, the assessment not only suffers from low efficiency but also inconsistent results depending on the operators’ skills. This study proposes a residual pixel-wise semantic segmentation, which segmented the fetal heart and thoracic contours in a 4-chamber view for assessing an enlarged fetal heart condition. The accuracy of intersection-over-union (IoU) and dice coefficient similarity (DCS) is used for model validation to further regulate the evaluation procedure. We use 1174 US images, comprising about 560 enlarged heart images, and about 614 normal heart images. Out of these data, 248 images are used for unseen data, and the remaining for training/validation processes. The performance of the proposed model, when tested on unseen data, achieved satisfactory results with 97.71% accuracy, 90.36% IoU, and 94.93% DCS. These metrics collectively demonstrate the satisfactory performance of the proposed model compared to existing segmentation models. The outcomes underscore that the proposed model establishes a state-of-the-art standard for enlarged fetal heart detection.

Multiclass instance segmentation optimization for fetal heart image object interpretation Syaputra, Hadi; Nurmaini, Siti; Partan, Radiyati Umi; Roseno, Muhammad Taufik
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 14, No 5: October 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v14.i5.pp4137-4150

This research aims to develop a multi-class instance segmentation model for segmenting, detecting, and classifying objects in fetal heart ultrasound images derived from fetal heart ultrasound videos. Previous studies have performed object detection on fetal heart images, identifying nine anatomical classes. Further, these studies have conducted instance segmentation on fetal heart images for six anatomical classes. This research seeks to expand the scope by increasing the number of classes to ten, encompassing four main chambers left atrium (LA), right atrium (RA), left ventricle (LV), right ventricle (RV); four valves tricuspid valve (TV), pulmonary valve (PV), mitral valve (MV), and aortic valve (AV); one aorta (Ao), and the spine. By developing an instance segmentation method for segmenting ten anatomical structures of the fetal heart, this research aims to make a significant contribution to improving medical image analysis in healthcare. It also aims to pave the way for further research on fetal heart diseases using AI. The instance segmentation approach is expected to enhance the accuracy of segmenting fetal heart images and allow for more efficient identification and labeling of each anatomical structure in the fetal heart.

Heart Chamber Segmentation in Cardiomegaly Conditions Using the CNN Method with U-Net Architecture Saputra, Tommy; Nurmaini, Siti; Roseno, Muhammad Taufik; Syaputra, Hadi
Jurnal Sisfokom (Sistem Informasi dan Komputer) Vol. 12 No. 3 (2023): NOVEMBER
Publisher : ISB Atma Luhur

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32736/sisfokom.v12i3.1976

Cardiomegaly is a disease in which sufferers show no symptoms and have symptoms such as shortness of breath, abnormal heartbeat and edema. Cardiomegaly will cause the sufferer's heart to pump harder than usual. Early diagnosis of cardiomegaly can help make decisions about whether the heart is abnormal or normal. In addition, due to the problem that manual examination takes time and requires human interpretation and experience, tools are needed to automatically develop and identify normal and abnormal hearts. Therefore, this study proposes cardiac chamber segmentation using 2D (two-dimensional) ultrasound convolutional neural networks for rapid cardiomegaly screening in clinical applications based on heart ultrasound examination. The proposed approach uses a CNN with a U-Net architecture model with abnormal and normal heart data. The research results obtained used the pixel matrix evaluation Avg_accuracy of 99.50%, Val_accuracy of 97.98% and Mean_IoU of 90.01%.

Title

Found 5 Documents
Search

Abstract

Abstract

Abstract

Abstract

Abstract

Title Search

Found 5 Documents Search

Abstract

Abstract

Abstract

Abstract

Abstract

Title

Found 5 Documents
Search