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

Sun, Chaoyu; Hong, Xiaojian; Sheng, Shi; Shen, Zhiyuan; Zhang, Huidi; Zhou, Lin-Xiao

doi:10.1109/access.2021.3056448

Cited by 8 publications

(2 citation statements)

References 22 publications

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“…The author's previous work [13] has proven that the deep learning algorithms have sufficient research potential for accurately detecting U-RBCs. To validate the performance of this proposed D-MVF on a multi-focus video dataset, two sets of comparing experiments for U-RBC target detection are carried out using four high-performance deep learning models: Faster R-CNN [28], Cascade R-CNN [29], RetinaNet [30], and YOLOv4 (applied in this proposed framework). Among them, Faster R-CNN and Cascade R-CNN are the state-of-the-art models in the field of U-RBC detection [12].…”

Section: Comparison Between Different U-rbc Detection Methodsmentioning

confidence: 99%

An Accurate Urine Red Blood Cell Detection Method Based on Multi-Focus Video Fusion and Deep Learning with Application to Diabetic Nephropathy Diagnosis

Hao

et al. 2022

Electronics

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Background and Objective: Detecting urine red blood cells (U-RBCs) is an important operation in diagnosing nephropathy. Existing U-RBC detection methods usually employ single-focus images to implement such tasks, which inevitably results in false positives and missed detections due to the abundance of defocused U-RBCs in the single-focus images. Meanwhile, the current diabetic nephropathy diagnosis methods heavily rely on artificially setting a threshold to detect the U-RBC proportion, whose accuracy and robustness are still supposed to be improved. Methods: To overcome these limitations, a novel multi-focus video dataset in which the typical shape of all U-RBCs can be captured in one frame is constructed, and an accurate U-RBC detection method based on multi-focus video fusion (D-MVF) is presented. The proposed D-MVF method consists of multi-focus video fusion and detection stages. In the fusion stage, D-MVF first uses the frame-difference data of multi-focus video to separate the U-RBCs from the background. Then, a new key frame extraction method based on the three metrics of information entropy, edge gradient, and intensity contrast is proposed. This method is responsible for extracting the typical shapes of U-RBCs and fusing them into a single image. In the detection stage, D-MVF utilizes the high-performance deep learning model YOLOv4 to rapidly and accurately detect U-RBCs based on the fused image. In addition, based on U-RBC detection results from D-MVF, this paper applies the K-nearest neighbor (KNN) method to replace artificial threshold setting for achieving more accurate diabetic nephropathy diagnosis. Results: A series of controlled experiments are conducted on the self-constructed dataset containing 887 multi-focus videos, and the experimental results show that the proposed D-MVF obtains a satisfactory mean average precision (mAP) of 0.915, which is significantly higher than that of the existing method based on single-focus images (0.700). Meanwhile, the diabetic nephropathy diagnosis accuracy and specificity of KNN reach 0.781 and 0.793, respectively, which significantly exceed the traditional threshold method (0.719 and 0.759). Conclusions: The research in this paper intelligently assists microscopists to complete U-RBC detection and diabetic nephropathy diagnosis. Therefore, the work load of microscopists can be effectively relieved, and the urine test demands of nephrotic patients can be met.

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Section: Comparison Between Different U-rbc Detection Methodsmentioning

confidence: 99%

An Accurate Urine Red Blood Cell Detection Method Based on Multi-Focus Video Fusion and Deep Learning with Application to Diabetic Nephropathy Diagnosis

Hao

et al. 2022

Electronics

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“…The most common examples of one-stage object detectors are YOLO [34,35], SSD [36,37], SqueezeDet [38], and DetectNet [39,40]. Two-stage methods prioritize detection accuracy, and typical models include Faster R-CNN [41,42], Mask R-CNN [43,44] and Cascade R-CNN [45,46].…”

Section: Related Work 21 Object Detectionmentioning

confidence: 99%

PDC: Pearl Detection with a Counter Based on Deep Learning

Hou

Dong²,

et al. 2022

Sensors

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Pearl detection with a counter (PDC) in a noncontact and high-precision manner is a challenging task in the area of commercial production. Additionally, sea pearls are considered to be quite valuable, so the traditional manual counting methods are not satisfactory, as touching may cause damage to the pearls. In this paper, we conduct a comprehensive study on nine object-detection models, and the key metrics of these models are evaluated. The results indicate that using Faster R-CNN with ResNet152, which was pretrained on the pearl dataset, mAP@0.5IoU = 100% and mAP@0.75IoU = 98.83% are achieved for pearl recognition, requiring only 15.8 ms inference time with a counter after the first loading of the model. Finally, the superiority of the proposed algorithm of Faster R-CNN ResNet152 with a counter is verified through a comparison with eight other sophisticated object detectors with a counter. The experimental results on the self-made pearl image dataset show that the total loss decreased to 0.00044. Meanwhile, the classification loss and the localization loss of the model gradually decreased to less than 0.00019 and 0.00031, respectively. The robust performance of the proposed method across the pearl dataset indicates that Faster R-CNN ResNet152 with a counter is promising for natural light or artificial light peal detection and accurate counting.

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