Overview of fine-tuning CNN-Based Models for X-ray Image Classification

被引：0

作者：

Ngoc Ha Pham ^{[1
]}

Giang Son Tran ^{[2
]}

机构：

[1] FPT Univ, Informat & Commun Technol Dept, Hanoi, Vietnam

[2] Vietnam Acad Sci & Technol, ICT Lab, Univ Sci & Technol Hanoi, Hanoi, Vietnam

来源：

PROCEEDINGS OF THE 2024 9TH INTERNATIONAL CONFERENCE ON INTELLIGENT INFORMATION TECHNOLOGY, ICIIT 2024 | 2024年

关键词：

Convolution Neural Network; Deep Learning; Residual Neural Network; Pneumonia; Classification; PNEUMONIA;

D O I：

10.1145/3654522.3654572

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

A lung infection is usually the cause of pneumonia, a common medical condition. It irritates the lungs' tissues and reduces their functionality. The severity of pneumonia can vary from a minor illness to a serious one. Identifying the exact infection causing the problem can be difficult. Diagnosis is often based on symptoms and physical examination, sometimes with chest X-rays. On the other hand, reviewing chest X-rays is a challenging and subjective task. In this work, we improve the CNN architecture to improve the X-ray image classification score performance. The objective of this study is to evaluate the fine-tuning of ResNet50V2. The ensemble technique that has been recommended yields very strong classification results, outperforming other models with an improvement of almost 97% in accuracy.

引用

页码：186 / 196

页数：11

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