A comparative deep learning approach for image classification and retrieval in scientific publications

Nguyen Hoang Anh and Tran Thanh Dien *

* Corresponding author: Tran Thanh Dien (email: thanhdien@ctu.edu.vn)

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Received: 22 Sep 2025
Revised: 10 Oct 2025
Accepted: 15 Feb 2026
Published: 25 Mar 2026
DOI: 10.22144/ctujoisd.2026.016
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Nguyen, H. A., & Tran, T. D. (2026). A comparative deep learning approach for image classification and retrieval in scientific publications. CTU Journal of Innovation and Sustainable Development, 18(1), 156-168. https://doi.org/10.22144/ctujoisd.2026.016
Issue
Vol. 18 No. 1 (2026)
Section
Engineering Technology

Main Article Content

Abstract

This study presents a comparative analysis of state-of-the-art deep learning models–EfficientNetB0, MobileNetV2, and ResNet101–for image classification and content-based retrieval in scientific publications. A dataset of 4,303 images from 11 categories was curated from the Can Tho University Journal of Science and enhanced through tailored data augmentation strategies. The models were fine-tuned using transfer learning with hyperparameters optimised via Grid Search. Features were extracted using GlobalAveragePooling2D, and cosine similarity was combined with the FAISS library for efficient similarity search. Experimental results demonstrate a clear performance-efficiency trade-off: ResNet101 achieved the highest classification accuracy, while EfficientNetB0 and MobileNetV2 offered significant advantages in inference speed. A user-friendly web interface was developed to support practical image retrieval applications. These findings highlight the potential of deep learning in enhancing the management and integrity of scientific image resources.

Keywords: Comparative analysis, Content-based image retrieval (CBIR), Deep learning, FAISS, Grad-CAM, Image classification

Article Details

Conflict of Interest

The authors declare that there is no conflict of interest regarding the publication of this paper. The authors have no financial, professional, or personal relationships that could have influenced the research results or the interpretation of the findings.
Creative Commons License

© 2026 The authors. This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License.

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