@inproceedings{d76abce9626d46a7a13a8748b640c5bf,
title = "Artificial Intelligence Based Segmentation for Skin-Layer Visualization from Optical Coherence Tomography Images",
abstract = "Changes in epidermal thickness are linked to various skin diseases, such as diabetic foot. Optical Coherence Tomography (OCT), a noninvasive imaging technology, enables detailed visualization of skin layers. This study employed a deep learning approach using the U-Net architecture to analyze OCT images, specifically focusing on the stratum corneum (SC) and epidermis (ED) layers. Utilizing a dataset of 10,000 images, the U-Net model achieved 92\% accuracy in predicting SC and 95\% accuracy for ED. These results highlight the potential of this method for accurate 3D visualization of SC and ED layers, paving the way for precise assessment of skin layer thickness and its changes in medical applications.",
keywords = "Deep Learning, Epidermis, Prediction, Stratum Corneum, U-Net",
author = "Ramadhan, \{Gilang Titah\} and Yori Pusparani and Prisilla, \{Ardha Ardea\} and Shen, \{Wei Cheng\} and Cheng, \{Hsu Tang\} and Liau, \{Ben Yi\} and Jan, \{Yih Kuen\} and Chang, \{Wen Thong\} and Lin, \{Chih Yang\} and Lung, \{Chi Wen\}",
note = "This study was supported by a grant from the National Science and Technology Council, Taiwan (NSTC 113-2221-E-035-014).; 2025 IEEE International Conference on Consumer Electronics, ICCE 2025 ; Conference date: 11-01-2025 Through 14-01-2025",
year = "2025",
doi = "10.1109/ICCE63647.2025.10930075",
language = "English (US)",
series = "Digest of Technical Papers - IEEE International Conference on Consumer Electronics",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2025 IEEE International Conference on Consumer Electronics, ICCE 2025",
address = "United States",
}