Privacy-Preserving Content-Based Medical Image Retrieval Using Integrated CNN Fusion and Quantization Optimization

Content‑Based Image Retrieval (CBIR) systems have become increasingly crucial in healthcare as the volume of medical imaging data continues to grow exponentially. However, existing systems struggle to balance privacy preservation, computational efficiency and retrieval accuracy, particularly in resource‑constrained healthcare environments. This research proposes a novel multi‑level privacy‑preserving CBIR architecture that integrates multiple convolutional neural network (CNN) architectures with fusion strategies and quantization optimization specifically designed for encrypted medical images. The proposed framework addresses three key challenges: privacy preservation through advanced encryption techniques, feature extraction using optimized CNN fusion strategies and computational efficiency through model quantization. By implementing multiple pre‑trained CNN models—including VGG‑16, ResNet50, DenseNet121 and EfficientNet‑B0—along with various fusion strategies, the system achieves improved feature extraction from encrypted medical images. The framework incorporates quantization techniques to optimize computational efficiency without compromising retrieval accuracy. Experimental results across multiple medical imaging modalities, including X‑ray, magnetic resonance imaging (MRI) and computed tomography (CT) scans, demonstrate the effectiveness of the proposed approach in terms of retrieval accuracy, computational efficiency and security robustness. This research contributes to advancing privacy‑preserving medical image analysis by providing a comprehensive solution that effectively balances security requirements with practical implementation constraints in healthcare settings.

@article{sadik2025,
  title     = {Privacy-Preserving Content-Based Medical Image Retrieval Using Integrated CNN Fusion and Quantization Optimization},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {16},
  number    = {8},
  year      = {2025},
  publisher = {The Science and Information Organization},
  author    = {Mohamed Jafar sadik and Muhammed E Abd Alkhalec Tharwat and Noor Azah Samsudin and Ezak Fadzrin Bin Ahmad},
  doi       = {10.14569/IJACSA.2025.0160848},
  url       = {https://doi.org/10.14569/IJACSA.2025.0160848}
}