Advancements of Deep Learning-Based Pneumonia Chest Classification

Yupeng Tong

2024

Abstract

Pneumonia, a severe respiratory illness with high mortality and morbidity rates, requires early and accurate diagnosis to ensure timely treatment. This paper explores the application of deep-learning techniques for pneumonia chest classifying based on medical image modalities such as X rays and Computed Tomography (CT) scanners. The methodology includes a framework for deep-learning-based pneumonia chest classification, which includes data collection, preprocessing and model development. The study uses a variety of deep learning architectures including Convolutional Neural Networks, Artificial Neural Networks, and Vision Transformers. The dataset is a large collection of chest X-rays and CT images that are preprocessed to improve model performance. This dataset is used to train deep learning models using advanced techniques like transfer learning, data enhancement, and architectural improvements. The performance of the model is evaluated with appropriate metrics and techniques such as SHapley Additive exPlanations (SHAP) are used to enhance interpretability. And the deep-learning techniques’ application for pneumonia chest classification has shown promising results in terms of accuracy and efficiency. The study highlights the importance for deep learning in the area such as pneumonia classification and stresses the importance of addressing limitations to enable practical implementation.

Paper Citation

in Harvard Style

Tong Y. (2024). Advancements of Deep Learning-Based Pneumonia Chest Classification. 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 564-568. DOI: 10.5220/0012959300004508

in Bibtex Style

@conference{emiti24,
author={Yupeng Tong},
title={Advancements of Deep Learning-Based Pneumonia Chest Classification},
booktitle={Proceedings of the 1st International Conference on Engineering Management, Information Technology and Intelligence - Volume 1: EMITI},
year={2024},
pages={564-568},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012959300004508},
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 - Advancements of Deep Learning-Based Pneumonia Chest Classification
SN - 978-989-758-713-9
AU - Tong Y.
PY - 2024
SP - 564
EP - 568
DO - 10.5220/0012959300004508
PB - SciTePress