C2PSA-Enhanced YOLOv11: Boosting Pneumonia Detection in Chest X-rays Through Pseudo-Color Attention Mechanisms

Yuchen Li

doi:10.47297/wspbdsWSP2752-630534.20250502

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C2PSA-Enhanced YOLOv11: Boosting Pneumonia Detection in Chest X-rays Through Pseudo-Color Attention Mechanisms

Volume 5, Issue 2

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C2PSA-Enhanced YOLOv11: Boosting Pneumonia Detection in Chest X-rays Through Pseudo-Color Attention Mechanisms
C2PSA-Enhanced YOLOv11: Boosting Pneumonia Detection in Chest X-rays Through Pseudo-Color Attention Mechanisms
Guangdong Polytechnic Normal University, Guangzhou, Guangdong, 517500, China
Vol. 5, Issue 2, Pages: 183-187(2025)
Published：18 June 2025
DOI：10.47297/wspbdsWSP2752-630534.20250502
Accepted：

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Li Yuchen.C2PSA-Enhanced YOLOv11: Boosting Pneumonia Detection in Chest X-rays Through Pseudo-Color Attention Mechanisms[J].Biomedical Data Science,2025,05(02):183-187.
DOI：

Li Yuchen.C2PSA-Enhanced YOLOv11: Boosting Pneumonia Detection in Chest X-rays Through Pseudo-Color Attention Mechanisms[J].Biomedical Data Science,2025,05(02):183-187. DOI： 10.47297/wspbdsWSP2752-630534.20250502.

摘要

Abstract

Pneumonia

a common respiratory infection

has a serious impact on human lives worldwide. In this paper

the editors propose a Yolov11 deep learning model that introduces an attention mechanism and present a data enhancement technique for medical imaging. After training and tuning on a public dataset and doing comparative experiments with other deep learning models

the model can efficiently detect lung X-images and determine whether it has pneumonia or not. The experimental data show that the accuracy

precision

recall

and F1-score of the model are 90.12%

91.45%

90.66%

and 91.05%

respectively

and have excellent results in the comparison experiments with four common architectures

namely

Yolov11

Yolov8

ResNet18

and ResNet50

which confirms the superiority of the model. By comparing the performance of the model before and after data enhancement

it was concluded that pseudo-coloring of gray medical images can improve the accuracy of the model by 4-7%.

关键词

Keywords

references

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