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All Journal International Journal of Electrical and Computer Engineering Journal of Applied Data Sciences
Hien, Lam Thanh
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Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering

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Journal : International Journal of Electrical and Computer Engineering

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An algorithm for decomposing variations of 3D model Phuong, Tran Thanh; Hien, Lam Thanh; Duc Vinh, Ngo; Manh Toan, Ha; Nang Toan, Do
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 2: April 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i2.pp1928-1936

Abstract

In recent times, there has been an increasing number of people who are concerned about the virtual reality field. Parameterization of deformations of 3D models is a meaningful problem in theoretical research and application development of virtual reality. This paper proposes a technique for conditional decomposition of 3D model variations based on a given set of 3D observations of an object, along with a set of input strain weights. The proposed algorithm is conducted through an optimal iterative process with solving the non-negative least squares problem. The output of the technique is a set of base models corresponding to different types of strain. The result of the proposed technique allows the creation of a new 3D model variant of the object in a simple and visually observable way. The algorithm has been tested and proven effective on data that are 3D face models created from the Japanese Female Facial Expression (JAFFE) dataset with labeled expression weights.
A framework for 3D radiotherapy dose prediction using the deep learning approach Hien, Lam Thanh; Toan, Ha Manh; Toan, Do Nang
International Journal of Electrical and Computer Engineering (IJECE) Vol 14, No 5: October 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v14i5.pp5524-5533

Abstract

Cancer is known as a dangerous disease to humans with a very high death rate. There are a lot of cancer treatment methods that have been studied and applied in the world. One of the main methods is using radiation beams to kill cancer cells. This method, also known as radiotherapy, requires experts having a high level of skill and experience. Our work focuses on the 3D dose prediction problem in radiotherapy by proposing a framework aiming to create a medical intelligent system for this problem. To do that, we created a convolutional neural network based on ResNet and U-Net to generate the predicted radiation dose. To improve the quality of the training phase, we also applied some data processing techniques based on the characteristics of the 3D computed tomography (CT) data. The experiment used the dataset from patients who were cancer-treated with radiotherapy in the OpenKBP competition. The results achieved good evaluating metrics, the first is by the Dose-score and the second is by the dose-volume histogram (DVH) score. From the training result, we built the medical system supporting 3D dose prediction and visualizing the result as slices in heatmap form.
Detecting lung nodules in computed tomography images based on deep learning Hien, Lam Thanh; Tu, Le Anh; Hieu, Pham Trung; Duc, Pham Minh; Nang, Nguyen Van; Toan, Do Nang
International Journal of Electrical and Computer Engineering (IJECE) Vol 15, No 6: December 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijece.v15i6.pp5604-5615

Abstract

Lung cancer is currently recognized as one of the most dangerous cancers, with high mortality rate. In order to deal with lung cancer, an important task is to detect lung nodules early to improve patient survival rates, and computed tomography (CT) scans are crucial data for this. In this research, we propose a deep learning-based method for detecting lung nodules in the CT images with the goal of increasing the likelihood of nodule appearance in the input data of the network, making it easier for the model to focus on relevant areas while reducing noise from areas unrelated to the result. Specifically, we propose a simple lung region segmentation process and optimize the hyperparameters of the faster region-based convolutional neural networks (faster R-CNN) model based on the analysis of nodule characteristics in CT image data. In our experiments, to evaluate the effectiveness of our proposals, we conducted tests on the standard LUNA16 dataset with different backbone configurations for the model, namely ResNet50, ResNet50v2, and MobileNet. The best results achieved were 0.86 mAP50 and 0.91 Recall for the Resnet50, and 0.84 mAP50 and 0.94 Recall for the ResNet50v2. These impressive outcomes underscore the success of our method and establish a robust basis for future studies to further integrate AI into healthcare solutions.
Co-Authors Duc Vinh, Ngo Duc, Pham Minh Hieu, Pham Trung Manh Toan, Ha Nang Toan, Do Nang, Nguyen Van Phan Thanh Tam, Phan Thanh Phuong, Tran Thanh Toan, Do Nang Toan, Ha Manh Tu, Le Anh