Preference-Driven Medical Image Retrieval using a Dual-Head DenseNet-121 and Multi-Objective Skyline Query for COVID-19 Detection

Slamet Handoko Handoko (Department of Electrical Engineering, Politeknik Negeri Semarang, Indonesia)
Prayitno Prayitno (Research and Development Division, SEAMEO SEAMOLEC, Indonesia)
Silvester Tena (Department of Electrical Engineering, Universitas Cendana, Indonesia)
Karisma Trinanda Putra (Department of Electrical Engineering, Universitas Muhammadiyah Yogyakarta, Indonesia)
Sunardi Sunardi (Department of Mechanical Engineering, Universitas Muhammadiyah Yogyakarta, Indonesia)
Eko Prasetyo (Department of Mechanical Engineering, Universitas Muhammadiyah Yogyakarta, Indonesia)
Cahya Damarjati (Department of Mechanical Engineering, Universitas Muhammadiyah Yogyakarta, Indonesia)

Jurnal Teknik Informatika (JUTIF)
Vol. 7 No. 3 (2026): JUTIF Volume 7, Number 3, June 2026

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.