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Issue: Số E11. 07.2026 HONG DUC UNIVERSITY JOURNAL OF SCIENCE
Section: Các bài báo
DOI: 10.70117/hdujs.E11.7.2026.1357
Date Published: 03/10/2026
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How to Cite
Pham, A. D. (2026). A UNIFIED FRAMEWORK WITH SPATIO-TEMPORAL AND MULTI-SCALE FEATURE LEARNING FOR 3D LUNG NODULE DETECTION. Hong Duc University Journal of Science, 7, 20-28. https://doi.org/10.70117/hdujs.E11.7.2026.1357

A UNIFIED FRAMEWORK WITH SPATIO-TEMPORAL AND MULTI-SCALE FEATURE LEARNING FOR 3D LUNG NODULE DETECTION

Pham Anh Duc1,
1 Trường Đại Học Hồng Đức

Main Article Content

Abstract

Lung nodule detection in CT scans remains a challenging computer vision task due to structural ambiguity and limited annotations. This paper introduces I3DR-Net, a one-stage 3D deep learning framework for joint detection and classification. The model combines a pre-trained I3D backbone for spatio-temporal feature extraction with a modified FPN for enhanced multi-scale representation. Experimental results show competitive performance (≈70–73% accuracy), demonstrating that transfer learning and improved pyramid design effectively enhance detection robustness for computer-aided diagnosis applications.

Keywords

Lung nodule detection, classification, CT-scan, transfer learning, feature pyramid network.

Article Details

References

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