Cascade Faster R-CNN Detection for Vulnerable Plaques in OCT Images

Through the application of optical coherence tomography (OCT), computerized medical image analysis can provide intelligent, auxiliary diagnosis for patients with atherosclerosis. In order to higher-resolution imaging interventions, a novel deformable cascade faster region-based convolutional neural networks (Faster R-CNN) is proposed realize vascular plaque recognition in OCT images. The network designed lumen feature detection, and convolution region interest pooling are adapted anchor scaling offset changes, thereby improving classification precision. To deal problem limited samples, an innovative, multiple task loss function established online instant training unlabeled samples. Experimental results prove that Faster R-CNN outperforms original method terms accuracy. Thus, effective at recognizing vulnerable plaques.