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http://dx.doi.org/10.6109/jkiice.2021.25.8.1046

Comparison and analysis of chest X-ray-based deep learning loss function performance  

Seo, Jin-Beom (Department of information Security, Daejeon University)
Cho, Young-Bok (Department of information Security, Daejeon University)
Publication Information
Journal of the Korea Institute of Information and Communication Engineering / v.25, no.8, 2021 , pp. 1046-1052 More about this Journal
Abstract
Artificial intelligence is being applied in various industrial fields to the development of the fourth industry and the construction of high-performance computing environments. In the medical field, deep learning learning such as cancer, COVID-19, and bone age measurement was performed using medical images such as X-Ray, MRI, and PET and clinical data. In addition, ICT medical fusion technology is being researched by applying smart medical devices, IoT devices and deep learning algorithms. Among these techniques, medical image-based deep learning learning requires accurate finding of medical image biomarkers, minimal loss rate and high accuracy. Therefore, in this paper, we would like to compare and analyze the performance of the Cross-Entropy function used in the image classification algorithm of the loss function that derives the loss rate in the chest X-Ray image-based deep learning learning process.
Keywords
Deep learning; Biomarker; Loss function; Artificial intelligence; Medical image;
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