Publications
My academic publications and research papers in machine learning, data science, and scientific computing.Last updated: 2025-05-17 by linhduongtuan
Detection of tuberculosis from chest X-ray images: Boosting the performance with vision transformer and transfer learning
Duong, L.T., Nguyen, P.T., Iovino, L., Pettersen, M.
Expert Systems with Applications, 2024
This paper presents a comprehensive approach to tuberculosis detection from chest X-ray images using vision transformers and transfer learning. We demonstrate significant improvements in diagnostic accuracy compared to traditional convolutional neural networks, achieving state-of-the-art performance on multiple datasets including the Montgomery and Shenzhen TB datasets.
Automatic detection of Covid-19 from chest X-ray and lung computed tomography images using deep neural networks and transfer learning
Duong, L.T., Nguyen, P.T., Iovino, L., Pettersen, M.
Applied Soft Computing, 2023
We propose an automated system for COVID-19 detection using both chest X-ray and CT images. Our approach combines multiple deep learning architectures with transfer learning techniques, achieving high sensitivity and specificity in distinguishing COVID-19 cases from normal and other pneumonia cases.
Fusion of edge detection and graph neural networks for classifying electrocardiogram signals
Duong, L.T., Vo, N.H., Nguyen, P.T., Iovino, L.
Expert Systems with Applications, 2023
This research introduces a novel approach combining edge detection techniques with graph neural networks for ECG signal classification. Our method converts ECG signals into graph representations, enabling the application of GNNs for improved arrhythmia detection and classification accuracy.
BLOOM-LoRA: Low-Rank Adaptation for Large Language Models in Medical Domain
Duong, L.T., Nguyen, T.H., Pham, M.D.
arXiv preprint, 2023
We present BLOOM-LoRA, a parameter-efficient fine-tuning approach for adapting the BLOOM large language model to medical applications. Our method uses Low-Rank Adaptation (LoRA) to fine-tune BLOOM on medical dialogue datasets, achieving significant improvements in medical question answering while maintaining computational efficiency.