BROWSE
Detail
Conference Paper
Non-invasive Prediction of Hemoglobin using Picture of Conjunctiva
Cited 0 time in 
Share 
Share
- 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
- 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