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All Journal Journal of Technology Informatics and Engineering Journal of Science and Mathematics Education
Tan, Wei Ling
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Agriculture, Biological Sciences & Forestry Biochemistry, Genetics & Molecular Biology Chemical Engineering, Chemistry & Bioengineering Chemistry Computer Science & IT Education Materials Science & Nanotechnology Mathematics Physics

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3D Medical Image Reconstruction through Transformer-Based Neural Networks: A Comparative Study Tan, Wei Ling; Menon, Arjun
Journal of Technology Informatics and Engineering Vol. 4 No. 3 (2025): DECEMBER | JTIE : Journal of Technology Informatics and Engineering
Publisher : University of Science and Computer Technology

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.51903/jtie.v4i3.453

Abstract

Three-dimensional reconstruction of CT and MRI images remains a persistent challenge in medical imaging, where clinicians require high‐fidelity volumes that preserve subtle anatomical details while remaining computationally efficient. This study evaluates a transformer-based neural network against a conventional convolutional neural network (CNN) baseline to determine which architecture delivers superior reconstruction accuracy for clinical use. A standard deep learning pipeline was constructed, which included data curation, intensity normalization, and augmentation, prior to training the models. The experimental comparison studied two representative architectures, a 3D U-Net that served as the CNN benchmark, and a 3D Swin Transform, that served as the attention approach. The quantitative analysis showed that the transformer produced a higher Peak Signal-to-Noise-Ratio (35.8 dB vs 33.1 dB), better Structural Similarity Index Measure (0.942 vs 0.911), and better Dice coefficient (0.91 vs 0.87) with little differences with respect to inference time per volume. The visual analysis showed sharper cortical folds and clearer lesion edges, which radiologists linked with higher diagnostic confidence. The transformer’s ability to model global spatial dependencies and reduce noise artifacts facilitates accurate and clinically pertinent reconstructions. This study shows that transformer models can be computationally efficient but more precise than CNN alternatives, which support their implementation in hospital Picture Archiving and Communication Systems (PACS) and within future real time patient diagnostics workflows. Taken together, these findings support the collective efforts of engineers and healthcare providers to leverage future algorithmic improvements that can enhance patient care and the safety of imaging.
Augmented Reality–Supported Learning and Its Effect on Secondary Students’ Conceptual Mastery of Geometry at Nanyang Girls' High School Tan, Wei Ling; Aishah, Siti
Journal of Science and Mathematics Education Vol. 2 No. 1 (2026): Journal of Science and Mathematics Education, March 2026
Publisher : Lembaga Publikasi Ilmiah Nusantara

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.70716/josme.v2i1.407

Abstract

Mathematics education, particularly in the domain of three-dimensional geometry, often demands high-level spatial visualization skills, posing a significant cognitive barrier for many secondary students. Conventional teaching methods relying on two-dimensional representations are frequently insufficient to elucidate spatial complexities. Augmented Reality (AR) technology offers an innovative pedagogical solution by projecting virtual objects into the real environment, bridging the gap between abstract representations and concrete understanding. This study aims to empirically evaluate the effectiveness of AR-supported learning on geometry conceptual mastery among students at Nanyang Girls' High School. The research employed a quasi-experimental design with pre-test and post-test measures on non-equivalent control groups. Participants comprised secondary level students divided into two groups: an experimental group utilizing AR applications to interactively visualize, manipulate, and dissect geometric solids, and a control group using traditional text-and-image-based learning media. Assessment instruments focused on deep conceptual understanding of solid properties, geometric transformations, and spatial relationships. Statistical analysis revealed a significant difference in the mean post-test score improvement of the experimental group compared to the control group ($p < 0.05$). These findings indicate that the interactive visualization features offered by AR effectively assist students in constructing more accurate mental models and achieving stronger memory retention of complex geometric objects. Beyond cognitive gains, qualitative data also noted increased student motivation and active engagement during the exploration process. This study concludes that AR integration is a valid instructional strategy for enhancing geometry mastery and recommends the development of mathematics curricula that are more adaptive to immersive technologies at the secondary level.
Co-Authors Aishah, Siti Menon, Arjun