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All Journal IJCCS (Indonesian Journal of Computing and Cybernetics Systems) TELKOMNIKA (Telecommunication Computing Electronics and Control) Journal of Information Technology and Computer Science (JOINTECS) JITK (Jurnal Ilmu Pengetahuan dan Komputer) EDUMATIC: Jurnal Pendidikan Informatika Jurnal Tekno Kompak bit-Tech Information System Journal (INFOS) Jurnal Teknik Mesin, Industri, Elektro dan Informatika Intechno Journal : Information Technology Journal
I Made Artha Agastya
Universitas AMIKOM Yogyakarta
Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Education Electrical & Electronics Engineering Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Library & Information Science Mathematics

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RVS Architectures: Performance and Efficiency Trade-offs in CNN, Attention, and Mamba Models Evi Dewi Sri Mulyani; Andi Sunyoto; Robert Marco; I Made Artha Agastya
IJCCS (Indonesian Journal of Computing and Cybernetics Systems) Vol 20, No 4 (2026): October
Publisher : IndoCEISS in colaboration with Universitas Gadjah Mada, Indonesia.

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/ijccs.113093

Abstract

Retinal Vessel Segmentation (RVS) is crucial for diagnosing eye diseases, requiring deep learning models to accurately segment complex vascular structures. While initial architectures like U-Net established the baseline, traditional CNNs struggle with capturing long-range dependencies necessary for full vessel connectivity. This Systematic Literature Review (SLR) critically analyzes the performance and efficiency trade-offs across conventional CNNs, Attention-based U-Net variants, and the novel Mamba/State Space Model (SSM) hybrids. Evaluation is based on segmentation quality (AUC, Sensitivity) and computational cost (Parameters, FLOPs). The findings confirm that advanced Attention-based models successfully boost Sensitivity (SE)—critical for fine vessels—over the baseline. Crucially, the emerging Mamba Hybrids (e.g., CRMA-UNet) offer superior efficiency, capturing global context with linear complexity rather than the quadratic cost of early Transformers. This shift, coupled with the necessity for lightweight designs (e.g., LDMRes-Net at 0.072M parameters), confirms that Mamba/SSM hybrids represent the optimal balance, making them the most promising future direction for efficient clinical deployment
Co-Authors A. Salky Maulana Andi Sunyoto Ari Fitriyandhi Arief Setyanto Arifiyanto Hadinegoro, Arifiyanto Atika Dwi Cahyani Dini Oktarina Dwi Handayani Eli Pujastuti Evi Dewi Sri Mulyani Hafidz Sanjaya, Hafidz Herlambang Priantoro Ika Kemala Sawati Azzahra José Ramón Martínez Salio Kumara Ari Yuana Kusrini Mohamad Firdaus Muhammad Ricky Perdana Nazaruddin Ahmad Nila Feby Puspitasari Risca Yunita Robert Marco Sasongko, Theopilus Bayu Simone Martin Marotta Sofyan Pariyasto Suputa Suputa Taufik Aldri Kristian