Pneumonia detection using snapshot ensemble model

Name: Pyar, Kyi

Organization : University of Computer Studies

Email : kyipyar@ucsm.edu.mm

Name: Maung Zaw, Sai Maung

Organization : University of Computer Studies

Email : saimaungmaungzaw@ucsm.edu.mm

keyword: Snapshot Ensemble Model

LCSH: Pneumonia -- Classification

LCSH: Deep learning (Machine learning)

Abstract: Pneumonia is a serious and potentially lifethreatening disease that predominantly affects the elderly population. Early diagnosis plays a crucial role in saving lives. Medical imaging, particularly the use of deep learning techniques, has shown promise in aiding the detection and classification of pneumonia in patients based on their chest Xrays. This paper presents a novel system for pneumonia detection and classification, utilizing a deep learning snapshot ensemble model. The EfficientNet-B0 technique is employed to address the pneumonia identification problems. The system is trained on the chest X-ray images (pneumonia) Kermany dataset, which is a well-known dataset in the field. The proposed system demonstrates impressive performance, achieving an accuracy of 97% for pneumonia classification

King Mongkut's University of Technology North Bangkok. Central Library

Address: BANGKOK

Email: library@kmutnb.ac.th

Created: 2023

Modified: 2024-04-22

Issued: 2024-04-22

บทความ/Article

application/pdf

BibliograpyCitation : In Institute of Electrical and Electronics Engineers. The 27th International Computer Science and Engineering Conference 2023 (ICSEC 2023) (pp.157-162). Bangkok : Institute of Electrical and Electronics Engineers

eng

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