Heliyon (Jun 2024)
Enhanced object detection in pediatric bronchoscopy images using YOLO-based algorithms with CBAM attention mechanism
Abstract
Background and Objective: 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. Methods: 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. Results: 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 mAP0.5 and mAP0.5:0.95 of 88.27% and 55.39%, respectively. Conclusions: 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.
