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All Journal Jurnal Teknik Informatika (JUTIF)
Silvester Tena
Department of Electrical Engineering, Universitas Cendana, Indonesia
Computer Science & IT

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Preference-Driven Medical Image Retrieval using a Dual-Head DenseNet-121 and Multi-Objective Skyline Query for COVID-19 Detection Slamet Handoko Handoko; Prayitno Prayitno; Silvester Tena; Karisma Trinanda Putra; Sunardi Sunardi; Eko Prasetyo; Cahya Damarjati
Jurnal Teknik Informatika (Jutif) Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026
Publisher : Informatika, Universitas Jenderal Soedirman

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.52436/1.jutif.2026.7.3.5884

Abstract

This study addresses the limitation of single-objective content-based image retrieval in medical imaging, which fails to consider multiple clinical preferences such as image quality. The objective is to develop a preference-driven retrieval system for COVID-19 chest radiography images. A hybrid approach is proposed by integrating a Dual-Head DenseNet-121 model for feature extraction and quality regression with a multi-objective skyline query algorithm for retrieval optimization. The system evaluates multiple image quality dimensions, including sharpness, contrast, exposure, signal-to-noise ratio, and entropy. Experimental results demonstrate that the proposed method achieves 100% Pareto efficiency and improves diversity and hypervolume coverage compared to conventional methods. This approach provides a more flexible and effective multi-objective retrieval mechanism, contributing to the advancement of intelligent medical image retrieval systems in computer science.
Co-Authors Cahya Damarjati Eko Prasetyo Karisma Trinanda Putra Prayitno Prayitno Slamet Handoko Handoko Sunardi Sunardi