Article Per Year (5 Year)
p-Index From 2021 - 2026
0.408
P-Index
This Author published in this journals
All Journal Journal of Electronics, Electromedical Engineering, and Medical Informatics
Wiharto Wiharto
Department of Informatics, Faculty of Information Technology and Data Science, University of Sebelas Maret, Surakarta, Indonesia
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering

Published : 2 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 2 Documents
Search

 1 
Classification of Ultrasound Images Using ResNet-50 with a Convolutional Block Attention Module (CBAM) Bagus Tegar Zahir Afif; Wiharto Wiharto; Umi Salamah
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 1 (2026): January
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i1.1406

Abstract

Liver fibrosis staging is a crucial component in the clinical management of chronic liver disease because it directly affects prognosis, therapeutic decision-making, and long-term patient monitoring. Ultrasound imaging is widely used as a noninvasive diagnostic modality due to its safety, low cost, and broad accessibility. Nevertheless, ultrasound-based fibrosis assessment remains challenging because liver parenchymal echotexture often exhibits low contrast, speckle noise, and subtle inter-stage variations, particularly among adjacent METAVIR stages. These characteristics frequently limit the effectiveness of conventional convolutional neural networks, which tend to emphasize dominant global patterns while suppressing weak but clinically meaningful texture cues. This study presents a task-oriented integration of a Convolutional Block Attention Module into a ResNet-50 backbone to enhance feature discrimination for five-stage liver fibrosis classification using heterogeneous B-mode ultrasound images. Rather than introducing a new attention mechanism, the contribution lies in the systematic insertion of CBAM after residual outputs across multiple network stages, enabling repeated channel and spatial recalibration from low-level texture descriptors to higher-level semantic representations. To further improve robustness and reduce prediction variance, a stratified 5-fold training strategy is combined with logit-level ensemble inference, where logits from independently trained fold models are averaged prior to Softmax normalization. Experiments were conducted on a publicly available dataset comprising 6,323 ultrasound images acquired from two tertiary hospitals using multiple ultrasound systems, with fibrosis stages labeled from F0 to F4 according to histopathology-based METAVIR scoring. The proposed framework achieves a test accuracy of 98.34%and consistently high precision, recall, and F1 scores across all fibrosis stages, with the most pronounced improvement observed for intermediate stages. Statistical analysis based on paired fold-wise comparisons confirms that the performance gain over the baseline ResNet 50 model is statistically significant. These results demonstrate that combining lightweight attention-based feature refinement with logit ensemble inference effectively addresses the inherent challenges of ultrasound-based liver fibrosis staging and provides a reliable noninvasive decision support framework with strong potential for clinical application and future multicenter validation.
A Multimodal Graph Neural Network for Multiclass ADHD and ASD Classification with Leakage-Aware Evaluation Chofifatul Hidayah; Wiharto Wiharto; Esti Suryani
Journal of Electronics, Electromedical Engineering, and Medical Informatics Vol 8 No 4 (2026): October
Publisher : Department of Electromedical Engineering, POLTEKKES KEMENKES SURABAYA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.35882/jeeemi.v8i4.1801

Abstract

Neurodevelopmental disorders such as attention deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) share overlapping clinical symptoms, complicating diagnosis and motivating objective, data-driven approaches using neuroimaging and machine learning. Graph neural networks (GNNs) have shown strong performance in this domain, yet many existing studies rely on single-modality data, transductive learning, and feature selection procedures that may introduce information leakage and inflate reported accuracy. This study proposes a multimodal graph learning framework that integrates resting-state fMRI (rs-fMRI), structural MRI (sMRI), and demographic data for classifying ADHD, ASD, and healthy controls (HC). The framework integrates temporal stability-based functional connectivity, hybrid feature selection, and adaptive multi-graph learning to exploit complementary information across these modalities. Using the ADHD-200 and ABIDE datasets, the framework is evaluated under three protocols that progressively tighten control over information leakage: transductive learning with global feature selection, inductive learning with global feature selection, and inductive learning with fold-wise feature selection. Results show that classification performance is highest under the transductive, globally-selected setting (85.5% accuracy for HC vs ADHD vs ASD, 92.5% for HC vs ASD, and 90.4% for HC vs ADHD), but decreases under the strictest leakage-aware protocol (70.9%, 81.7%, and 79.3%, respectively). This performance gap indicates that conventional evaluation protocols can substantially overestimate real-world generalization. Importantly, the proposed framework still achieves reasonable accuracy under the strictest setting, suggesting genuine discriminative capability beyond evaluation artifacts. These findings emphasize that leakage-aware evaluation, although yielding lower numbers, provides a more realistic and trustworthy estimate of model performance, highlighting its importance for developing reliable neuroimaging-based GNN models
Co-Authors Bagus Tegar Zahir Afif Chofifatul Hidayah Esti Suryani Umi Salamah