Early View
PAPER ID: 1522
TITTLE: Enhancing Accuracy in Classification Models for Skin Disease Diagnosis based on Segformer and ConvNeXt Approach
AUTHOR(S): Hai-Duong Le, Van-Dung Hoang*
ABSTRACT:
This study introduces an innovative methodology to enhance the precision of skin disease diagnosis classification models by integrating segmentation results. Employing advanced machine learning techniques, our approach involves predicting lesion areas in skin images by combining SegFormer for skin lesion segmentation and backbone ConvNeXt for classifying skin images that consist of benign and malignant diseases. Based on training the SegFormer model for skin lesion segmentation, it achieved the IoU (intersection over union) ratio of 0.861 on the test set, outperforming the top 1 entry on the ISIC 2018 Leaderboards, which had an IoU of 0.802. Furthermore, our skin classification model uses image cropping to generate input images that emphasize damaged skin areas, eliminating redundant information. Leveraging the segmentation model’s results, we define the bounding box for the lesion area, obtain a new image within the bounding box by adding padding, and then compare this new data with the original data. The disease classification model, using ConvNeXt as its backbone, exhibited superior performance on the new dataset compared with the original dataset, achieving a higher accuracy of 1.61%, precision of 26.42%, and recall of 26.49%. This research paves the way for novel approaches to address disease diagnosis challenges in medical images, particularly in skin diseases. It can improve the performance of classification models when trained on image datasets that do not have synchronization during acquisition.
VERSION OF RECORD ONLINE: 05 April 2024 VIEW PDF
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PAPER ID: 1738
TITTLE: Factors Affecting Information Technology Students’ Willingness to Use Generative Generative Artificial Intelligence in Learning: A UTAUT-Based Study
AUTHOR(S): Tran Ngoc Minh*, Viet-Tuan Le
ABSTRACT:
The rapid advancement of Generative Artificial Intelligence (GAI) has greatly influenced its adoption in various fields, particularly education. This study utilizes the Unified Theory of Acceptance and Use of Technology (UTAUT) to explore factors shaping the willingness of vocational and college-level information technology students to use GAI for learning specialized subjects. Conducted with 115 information technology students in Khanh Hoa province, Vietnam, the research focuses on four key factors: performance expectancy, effort expectancy, social influence, and facilitating conditions. The findings reveal that performance expectancy, social influence, and facilitating conditions play significant roles in positively influencing students' acceptance of GAI. These results highlight the potential of GAI as a transformative educational tool, emphasizing the need to integrate technology into teaching practices and foster an environment that supports its effective implementation in education.
VERSION OF RECORD ONLINE: 06 May 2025 VIEW PDF
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