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Conference Paper Non-invasive Prediction of Hemoglobin using Picture of Conjunctiva
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Authors
Minjun Kwon, In-su Jang, Kwang-Ju Kim
Issue Date
2023-08
Citation
International Conference on Platform Technology and Service (PlatCon) 2023, pp.118-122
Publisher
IEEE
Language
English
Type
Conference Paper
DOI
https://dx.doi.org/10.1109/PlatCon60102.2023.10255170
Abstract
This study proposes a comprehensive approach combining segmentation and regression techniques for predicting anemia severity using conjunctiva images. Anemia is a prevalent health condition with a global impact. The segmentation phase employs deep learning models to accurately segment the conjunctival area, allowing precise localization of anemia-related features. The regression phase utilizes pretrained deep-learning models to predict the severity of anemia based on the segmented images. Evaluation metrics, including Intersection over Union (IoU) for segmentation and Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) for regression, assess the accuracy of the predictions. Experimental results demonstrate the potential of this approach for non-invasive anemia severity prediction, contributing to improved healthcare outcomes.
KSP Keywords
Comprehensive Approach, Health condition, Healthcare outcomes, Improved healthcare, Mean Absolute Error, Non-invasive, Precise localization, Regression techniques, Segmented images, deep learning(DL), deep learning models