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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI)
Annastasya Nabila Elsa Wulandari
Universitas Harapan Bangsa
Computer Science & IT Electrical & Electronics Engineering

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Lightweight Hybrid Linformer-Mamba U-Net for Efficient Retinal Microaneurysm Segmentation Arif Setia Sandi Ariyanto; Deny Nugroho Triwibowo; Agriby Diandra Chaniago; Indah Trivilia; Annastasya Nabila Elsa Wulandari
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 11 No. 4 (2025): December
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v11i4.31598

Abstract

Diabetic retinopathy is a major microvascular complication of diabetes and a leading cause of vision loss among the working-age population. Microaneurysms (MAs), as the earliest clinical indicators of disease progression, remain challenging to segment due to their small size, low contrast, and extreme class imbalance. This study proposes a lightweight hybrid U-Net architecture for microaneurysm segmentation in retinal fundus images, designed to balance detection sensitivity and computational efficiency for deployment in resource-constrained environments. The proposed architecture integrates depthwise separable convolutions for efficient local feature extraction, a Transformer-Lite bottleneck based on Linformer self-attention for global contextual modeling, and a Mamba State Space Model (SSM)–based decoder to enhance feature propagation and spatial continuity.  The research contribution of this work is threefold: the introduction of an efficient hybrid U-Net combining Linformer and Mamba SSM for microaneurysm segmentation; a deployment-oriented evaluation protocol that explicitly distinguishes patch-level learning behavior from full-image reconstruction performance; and a transparent analysis of false positive behavior under extreme background dominance.  Experiments were conducted on the IDRiD dataset, consisting of 81 retinal images, using patient-level data splitting prior to patch extraction to prevent data leakage.  The results indicate that while patch-level evaluation demonstrates effective lesion-centric learning, deployment-realistic full-image evaluation reveals a notable performance degradation caused by false positive accumulation in extensive background regions. Nevertheless, the model maintains high recall, indicating preserved lesion sensitivity. These findings suggest that lightweight architectural design can deliver meaningful performance and is well suited for screening-oriented decision-support systems that prioritize efficiency and sensitivity.
Reproducible Biomedical NER and Proxy Relation Extraction for Drug–Adverse Event Analysis in Breast Cancer Deny Nugroho Triwibowo; Hadi Jayusman; Rachman Hidayat; Anisya; Annastasya Nabila Elsa Wulandari
Jurnal Ilmiah Teknik Elektro Komputer dan Informatika Vol. 12 No. 1 (2026): March
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26555/jiteki.v12i1.31594

Abstract

Pharmacovigilance requires automated systems to extract biomedical entities and their relationships from text, as manual processes are inefficient and prone to error. This study develops a reproducible pipeline for Named Entity Recognition (NER) and pattern-based proxy relation formation, focusing on drug side effects related to breast cancer. The research contribution is twofold: a domain-specific annotated dataset for pharmacovigilance NER, and a reproducible pipeline for proxy-based relation analysis. The experimental setup combines MobileBERT, DistilBERT, TinyBERT, and ALBERT. Evaluation is conducted using accuracy, precision, recall, F1-score, ROC AUC, and computational efficiency metrics. The results show that ALBERT achieves the highest NER performance (F1-score = 0.9261), while DistilBERT attains the best ROC AUC (0.9037). TinyBERT is the most efficient model, with 4.57 million parameters, 4.68 G FLOPs, and an average training time of 45.8 seconds per scenario. The proposed pipeline demonstrates a trade-off between accuracy and computational efficiency under the evaluated setting. The generated relations act as sentence-level proxy indicators of potential drug–adverse event associations and serve as a preliminary triage layer requiring expert validation rather than a high-precision system. However, the approach does not account for negation, uncertainty, or cross-sentence context, which may introduce false positive associations. Despite these limitations, the pipeline provides a reproducible baseline for exploratory pharmacovigilance analysis.
Co-Authors Agriby Diandra Chaniago Anisya Arif Setia Sandi A. Deny Nugroho Triwibowo Hadi Jayusman Indah Trivilia Rachman Hidayat