A Rotated Object Detection Model With Feature Redundancy Optimization for Coronary Athero-Sclerotic Plaque Detection

Coronary atherosclerotic plaques are a major cause of cardiovascular disease (CVD). Although there have been significant advancements in plaque-assisted diagnosis by deep learning methods utilizing intravascular optical coherence tomography (IVOCT), images still contain redundant features, including residual blood, thrombus, and extensive black backgrounds. Furthermore, the feature fusion process produces redundant information. These redundant features interfere with plaque feature extraction, resulting in decreased performance and increased computational complexity. Current methods for addressing redundant features rely mostly on simple feature selection or reduction methods, overlooking the significance of redundant features in model training. The Redundancy Feature Diminishment Strategy (RFDS) is designed to address this issue by dynamically adjusting the importance of redundant features during the training process. Meanwhile, RFDS is introduced into spatial and channel reconstruction convolution (SCConv) to form the enhancement structure RFDS-SCConv, which refines feature extraction by adaptively regulating redundant features in both spatial and channel dimensions. Based on RFDS-SCConv, a lightweight plaque detection framework, YOLO-Plaque, is proposed. RFDS-SCConv and FasterBlock are integrated into the framework to substantially reduce the interference of redundant features during feature extraction and fusion. This study evaluates the robustness of the YOLO-Plaque model by tenfold cross-validation utilizing 2504 IVOCT images collected from a collaborative project between the School of Software at Shanxi Agricultural University and Shanxi Provincial Coal Centre Hospital. Experimental results demonstrate that YOLO-Plaque performs better than existing plaque detection models while remaining lightweight. The vital metric mAP50 has increased by 3.59% reaching 91.23%, while GFLOPs and parameters decreased to 7.2 and 2.55M, respectively.