Udomchaiporn, Akadej

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Udomchaiporn, Akadej
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akadej.ud@kmitl.ac.th

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    Comparative Analysis of Deep Learning Models for Building Extraction from High-resolution Satellite Imagery
    (2025-01-01)
    Chueprasert, Tachasit
    ;
    Udomchaiporn, Akadej
    ;
    Intagosum, Sarun
    In this research, an approach to extract buildings from Google's satellite imagery was proposed. The performances of various deep learning models (U-Net, RIU-Net, U-Net++, Res-U-Net, and DeepLabV3+) on pre-processed datasets were compared. The models were trained using the similarity metrics of Intersection over Union (IoU) and Dice Similarity Coefficient (DSC). The best-performing models among the segmentation techniques were Res-U-Net and DeepLabV3+. Res-U-Net, an enhanced version of the traditional U-Net model that incorporates residual connections for improved feature propagation, achieved an F1 score of 85.43% when using the RGB dataset. Similarly, DeepLabV3+ also achieved high performance on the Enhanced RGB dataset, obtaining an F1 score of 85.18% after applying pre-processing techniques. This research highlights the significance of color as a dominant feature for accurate building extraction from satellite images. The findings contribute to improved methodologies for building identification, benefiting urban planning, and disaster management applications.
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    Cross-Domain Robust Liveness Detection: A Transfer Learning Approach for Combating Sophisticated Presentation Attacks in Mobile Authentication
    (2025-01-01)
    Kerdpramote, Phuvis
    ;
    Poomdeesittinon, Akeanant
    ;
    Jamsri, Rapeeploy
    ;
    Krueyos, Phanuwit
    ;
    Muangkan, Ronnakorn
    As biometric authentication systems become ubiquitous in Southeast Asia's digital economy, sophisticated presentation attacks using deepfakes, high-resolution displays, and 3D masks pose critical security threats. This paper presents a comprehensive cross-domain liveness detection framework that addresses the generalization challenges plaguing current systems. Our approach leverages MobileNetV2-based transfer learning with a novel two-phase training strategy, achieving superior cross-domain performance while maintaining computational efficiency for mobile deployment. We introduce domain-aware augmentation techniques and evaluate our system across multiple benchmark datasets including NUAA and a locally-collected Thai demographic dataset. Experimental results demonstrate 84.35% accuracy on NUAA and 78.62% cross-domain accuracy, with significant improvements in Attack Presentation Classification Error Rate (APCER) reduction from 28.7% to 15.4% compared to baseline methods. The system successfully detects emerging attack vectors including deepfake videos and tablet-based spoofing attempts. We provide comprehensive analysis of deployment challenges in resource-constrained environments demonstrating practical applicability for Thailand's mobile banking and digital identity verification ecosystem.
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    An artificial intelligence model for the diagnosis of otitis media with effusion in children
    (2026-01-01)
    Ungkanont, Kitirat
    ;
    Udomchaiporn, Akadej
    ;
    Sriphoonga, Nopavit
    ;
    Wannarong, Thanakrit
    ;
    Rugsujrit, Thaweewat
    Background: The diagnosis of otitis media with effusion (OME) requires substantial training and experience in otoscopic examination of children. Objective: This study developed an artificial intelligence (AI) model to predict OME diagnosis in children. Methods: The source data were images of pediatric patients’ tympanic membranes obtained by otoendoscopy. A convolutional neural network was used in machine learning. The diagnostic features of the tympanic membrane, as labelled by the experts, and the surgical findings served as the ground truth. InceptionV4 built the final model. The model was trained using the Adaptive Moment Estimation optimizer with an initial learning rate of 0.0001 and a total duration of 100 epochs. The batch size was 32. The Categorical Cross-Entropy loss function was employed for the internal validation. The outcome was to distinguish between OME and normal tympanic membrane. A confusion matrix was used to assess the model’s performance. The model was tested for agreement with otolaryngologists and implemented as a web application. Results: The initial sample size was 320 pictures. For OME, the model achieved an accuracy of 94.7% (95% CI 0.88, 1). The F1 score was 96% (95% CI 0.89, 1), and the area under the receiver operating characteristic curve was 0.98 (95% CI 0.93, 1). The kappa agreement between AI and experienced otolaryngologists was 0.627 (p < 0.001). Conclusion: An AI diagnostic model for otitis media with effusion had good accuracy and moderate agreement with otolaryngologists. The model should be helpful for preliminary diagnosis, telemedicine, or educational purposes.
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    Ensemble Model for Segmentation of Lateral Ventricles from 3D Magnetic Resonance Imaging
    (2020-01-01)
    Udomchaiporn, Akadej
    ;
    Lertrungwichean, Khitichai
    ;
    Klinkasen, Pokpakorn
    ;
    Nuchprasert, Chawanwut
    The paper proposes an ensemble model to segment lateral ventricles from 3D Magnetic Resonance Imaging (MRI) brain scan. Thresholding and Active Contour techniques combining with a noise removal method were applied to segment lateral ventricles from the brain images. The experiments were conducted by segmenting 73 MRI brain scans using our proposed model and then comparing their volumes to those using manual model conducted by an expert. The experimental results indicated that the proposed model segmented lateral ventricles as excellent as the manual model in terms of accuracy but outperformed the manual model in terms of time performance. The contribution of the paper is that the segmented lateral ventricles can be used for further analysis such as medical condition classification.