The Comprehensive Investigation of Lung Disease Classification Based on SGD

Yixiang Fan

2024

Abstract

Lung disease classification is an important research topic in the field of medical imaging. This paper explores the use of the stochastic gradient descent (SGD) algorithm for classifying lung diseases. Initially, it details the principles of the SGD algorithm and its application in lung disease classification. Following this, the paper summarizes existing research on childhood pneumonia and introduces a novel approach named Stochastic Gradient Descent with Warm Restarts Ensemble (SGDRE). This method combines an integration technique, random gradient descent, and a hot restart mechanism to address prevalent issues in deep learning and enhance the precision of early diagnosis. In the automatic detection of pneumonia, researchers use a new deep learning method to simplify the detection process of pneumonia and improve the accuracy by using deep transfer learning, and classify the bacteria and viruses of pneumonia. Finally, this study discussed the future research directions and challenges, including how to use interpretability algorithm, Transfer learning and Federated learning to further improve the interpretability of the model, the application of the system in different data sets, and the protection of patient privacy. This paper aims to provide researchers with a comprehensive understanding of lung disease classification using SGD algorithm.

Paper Citation

in Harvard Style

Fan Y. (2024). The Comprehensive Investigation of Lung Disease Classification Based on SGD. In Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI; ISBN 978-989-758-713-9, SciTePress, pages 435-439. DOI: 10.5220/0012939700004508

in Bibtex Style

@conference{emiti24,
author={Yixiang Fan},
title={The Comprehensive Investigation of Lung Disease Classification Based on SGD},
booktitle={Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI},
year={2024},
pages={435-439},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012939700004508},
isbn={978-989-758-713-9},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI
TI - The Comprehensive Investigation of Lung Disease Classification Based on SGD
SN - 978-989-758-713-9
AU - Fan Y.
PY - 2024
SP - 435
EP - 439
DO - 10.5220/0012939700004508
PB - SciTePress