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KMID : 1094720210260030369
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Biotechnology and Bioprocess Engineering
2021 Volume.26 No. 3 p.369 ~ p.374
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Analysis and Prediction of Surface Condition of Artificial Skin Based on CNN and ConvLSTM
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Jun Sang-Yoon
Shin Hwa-Sung
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Abstract
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Recently, with greater focus on ethical issues related to animal testing, interest in artificial skin platforms has increased in both cosmetic and medical industries. Artificial skin comprises dermal and epidermal layers. In particular, proper differentiation and proliferation of keratinocytes in the epidermal layer has a considerable influence on the role of the skin as a barrier. However, during 3D culture, real-time monitoring and evaluation of the tissue being cultured are difficult. In this study, Convolutional Neural Network and Convolutional Long Short-Term Memory were utilized for prediction of the artificial skin image. To evaluate the designed models, the similarity between the predicted artificial skin image was compared with the real image. We verified the possibility and practicability of artificial skin image analysis and prediction using neural network models. In the future, this approach could be applied to image prediction under certain conditions, such as inflammatory or skin diseases.
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KEYWORD
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artificial skin, image prediction, Convolutional Neural Network, ConvLSTM, deep learning, image analysis
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