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.

Medical ImagingTuberculosis DetectionVision TransformerTransfer LearningDeep Learning

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.

COVID-19 DetectionMedical ImagingDeep LearningTransfer LearningComputer Vision

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.

ECG ClassificationGraph Neural NetworksEdge DetectionBiomedical Signal ProcessingArrhythmia Detection

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.

Large Language ModelsMedical AILoRABLOOMParameter-Efficient Fine-tuning