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All Journal Jurnal Ilmiah Teknik Elektro Komputer dan Informatika (JITEKI) Journal of Advanced Health Informatics Research
Indah Trivilia
Universitas Harapan Bangsa
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Engineering Medicine & Pharmacology Public Health

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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.
Explainable AI for Mental Health and Biomedical Decision Systems: A Comprehensive Review Pramesti Dewi; Purwono Purwono; Annastasya Nabila Elsa Wulandari; Indah Trivilia
Journal of Advanced Health Informatics Research Vol. 4 No. 2 (2026)
Publisher : Peneliti Teknologi Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59247/jahir.v4i2.376

Abstract

The application of Artificial Intelligence (AI) in mental health is experiencing rapid development, while algorithmic transparency and clinical translation readiness still face fundamental obstacles. This review synthesizes empirical findings related to the use of Explainable Artificial Intelligence (XAI) in mental health and biomedical decision systems, focusing on three evaluative aspects, namely explainability architecture, validation strength, and clinical integration. The literature search followed the PRISMA 2020 guidelines across five major databases for publications from 2020 to 2026 and yielded nine studies that met the inclusion criteria. The synthesis results show the dominance of post-hoc approaches, particularly SHAP, which are commonly applied to ensemble and boosting models, while intrinsic models and counterfactual approaches are still rarely used. The majority of studies rely on internal validation, while independent external validation and prospective application in real clinical workflows are relatively limited. User-based evaluation of explainability has also been understudied, with algorithmic transparency more often understood as technical feature attribution rather than as a verified mechanism in clinical decision-making. These findings indicate a persistent gap between methodological advances and the level of clinical translation maturity. Explainability has not been systematically integrated with robust validation designs or user-oriented evaluations. This review proposes a translation evaluation framework that combines technical and clinical dimensions to assess the readiness for XAI implementation more comprehensively. The development of XAI in mental health requires evaluation standardization, strengthened external validation, and prospective testing focused on clinical impact and user trust.
Co-Authors Agriby Diandra Chaniago Annastasya Nabila Elsa Wulandari Arif Setia Sandi A. Deny Nugroho Triwibowo Pramesti Dewi Purwono, Purwono