Yan Jianqi, Zeng Yifan, Lin Junhong, Pei Zhiyuan, Fan Jinrui, Fang Chuanyu, Cai Yong
Faculty of Innovation Engineering, Macau University of Science and Technology, Avenida Wai Long, Taipa, 999078, Macau.
R&D Department, Quanbao Technologies Co. Ltd, Hagongda Road, Xiangzhou District, Zhuhai, 519087, China.
Heliyon. 2024 Jun 17;10(12):e32678. doi: 10.1016/j.heliyon.2024.e32678. eCollection 2024 Jun 30.
Bronchoscopy is a widely used diagnostic and therapeutic procedure for respiratory disorders such as infections and tumors. However, visualizing the bronchial tubes and lungs can be challenging due to the presence of various objects, such as mucus, blood, and foreign bodies. Accurately identifying the anatomical location of the bronchi can be quite challenging, especially for medical professionals who are new to the field. Deep learning-based object detection algorithms can assist doctors in analyzing images or videos of the bronchial tubes to identify key features such as the epiglottis, vocal cord, and right basal bronchus. This study aims to improve the accuracy of object detection in bronchoscopy images by integrating a YOLO-based algorithm with a CBAM attention mechanism.
The CBAM attention module is implemented in the YOLO-V7 and YOLO-V8 object detection models to improve their object identification and classification capabilities in bronchoscopy images. Various YOLO-based object detection algorithms, such as YOLO-V5, YOLO-V7, and YOLO-V8 are compared on this dataset. Experiments are conducted to evaluate the performance of the proposed method and different algorithms.
The proposed method significantly improves the accuracy and reliability of object detection for bronchoscopy images. This approach demonstrates the potential benefits of incorporating an attention mechanism in medical imaging and the benefits of utilizing object detection algorithms in bronchoscopy. In the experiments, the YOLO-V8-based model achieved a mean Average Precision (mAP) of 87.09% on the given dataset with an Intersection over Union (IoU) threshold of 0.5. After incorporating the Convolutional Block Attention Module (CBAM) into the YOLO-V8 architecture, the proposed method achieved a significantly enhanced and of 88.27% and 55.39%, respectively.
Our findings indicate that by incorporating a CBAM attention mechanism with a YOLO-based algorithm, there is a noticeable improvement in object detection performance in bronchoscopy images. This study provides valuable insights into enhancing the performance of attention mechanisms for object detection in medical imaging.
支气管镜检查是一种广泛应用于诊断和治疗感染、肿瘤等呼吸系统疾病的程序。然而，由于存在诸如黏液、血液和异物等各种物体，可视化支气管和肺部可能具有挑战性。准确识别支气管的解剖位置可能相当困难，尤其是对于该领域的新手医疗专业人员而言。基于深度学习的目标检测算法可以帮助医生分析支气管的图像或视频，以识别诸如会厌、声带和右基底支气管等关键特征。本研究旨在通过将基于YOLO的算法与CBAM注意力机制相结合，提高支气管镜检查图像中目标检测的准确性。
在YOLO-V7和YOLO-V8目标检测模型中实现CBAM注意力模块，以提高它们在支气管镜检查图像中的目标识别和分类能力。在此数据集上比较各种基于YOLO的目标检测算法，如YOLO-V5、YOLO-V7和YOLO-V8。进行实验以评估所提出方法和不同算法的性能。
所提出的方法显著提高了支气管镜检查图像目标检测的准确性和可靠性。这种方法证明了在医学成像中纳入注意力机制的潜在益处以及在支气管镜检查中使用目标检测算法的益处。在实验中，基于YOLO-V8的模型在给定数据集上，交并比（IoU）阈值为0.5时，平均精度均值（mAP）达到87.09%。将卷积块注意力模块（CBAM）纳入YOLO-V8架构后，所提出的方法分别显著提高到88.27%和55.39%。
我们的研究结果表明，通过将CBAM注意力机制与基于YOLO的算法相结合，支气管镜检查图像中的目标检测性能有显著提高。本研究为提高医学成像中目标检测的注意力机制性能提供了有价值的见解。
