基于YOLOv8s的改进结核病病原体检测算法
A novel tuberculosis pathogens detection algorithm based on YOLOv8s
乐振 1孙振 2鞠瑞文 2李庆党3
作者信息
- 1. 青岛科技大学信息科学技术学院,山东青岛 266061
- 2. 青岛科技大学数据科学学院,山东青岛 266061
- 3. 青岛科技大学中德科技学院,山东青岛 266061
- 折叠
摘要
针对结核病病原体在痰涂片图像背景复杂且目标小,人工筛查成本较高的问题,提出了一种基于YOLOv8s的结核病病原体检测方法.首先,采用基于空间和通道重构卷积改进的结构来限制特征冗余.其次,引入了坐标注意力来扩大模型的感受野.再者,使用空间金字塔池化跨阶段局部网络来提取不同尺度上的特征信息.最后,嵌入基于归一化的注意力机制抑制不太显著的特征.实验结果表明,在公开数据集上,改进网络模型与原YOLOv8s模型相比,精确率和召回率分别提升2.7%和1.5%,置信度为0.5时的平均精度均值提高了2.3%,该模型能够有效辅助影像科医师进行诊断.
Abstract
To address the challenges of detecting tuberculosis pathogens in sputum smear images,such as complex backgrounds,small targets,and high costs of manual screening,a detection method based on YOLOv8s is presented.The structure is improved through spatial and channel reconstruction convolutions to limit feature redundancy,and a coordinate attention is introduced to expand the receptive field of the model.Furthermore,a spatial pyramid pooling cross-stage partial network is used to extract feature information at different scales,and a normalized attention mechanism is embedded to suppress less significant features.The experimental results on a public dataset show that compared with the original YOLOv8s model,the improved network model enhances precision and recall rates by 2.7%and 1.5%,respectively,and improved mean average precision at a confidence level of 0.5 by 2.3%,demonstrating that the improved model can effectively assist radiologists in diagnosis.
关键词
结核病/深度学习/YOLOv8/注意力机制Key words
tuberculosis/deep learning/YOLOv8/attention mechanism引用本文复制引用
基金项目
山东省泰山学者项目(tshw201502042)
出版年
2024
NETL
NSTL
万方数据