Detection and Classification of Bronchiectasis Based on Improved Mask-RCNN

Ning, Yue; Zhang, Jingwei; Zhao, Jing; Zhang, Qinyan; Lin, Xin-Shan; Yang, Ji-Jiang

doi:10.3390/bioengineering9080359

Cited by 4 publications

(4 citation statements)

References 23 publications

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“…The application of AI in ultrasound imaging is currently a hot topic, especially in the fields of liver, cardiovascular, thyroid, and musculoskeletal systems [23][24][25][26]. AI techniques include conventional machine learning methods and deep learning methods.…”

Section: Discussionmentioning

confidence: 99%

Deep Learning Detection and Segmentation of Facet Joints in Ultrasound Images Based on Convolutional Neural Networks and Enhanced Data Annotation

Wu,

Xia,

Wang

et al. 2024

Diagnostics

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The facet joint injection is the most common procedure used to release lower back pain. In this paper, we proposed a deep learning method for detecting and segmenting facet joints in ultrasound images based on convolutional neural networks (CNNs) and enhanced data annotation. In the enhanced data annotation, a facet joint was considered as the first target and the ventral complex as the second target to improve the capability of CNNs in recognizing the facet joint. A total of 300 cases of patients undergoing pain treatment were included. The ultrasound images were captured and labeled by two professional anesthesiologists, and then augmented to train a deep learning model based on the Mask Region-based CNN (Mask R-CNN). The performance of the deep learning model was evaluated using the average precision (AP) on the testing sets. The data augmentation and data annotation methods were found to improve the AP. The AP50 for facet joint detection and segmentation was 90.4% and 85.0%, respectively, demonstrating the satisfying performance of the deep learning model. We presented a deep learning method for facet joint detection and segmentation in ultrasound images based on enhanced data annotation and the Mask R-CNN. The feasibility and potential of deep learning techniques in facet joint ultrasound image analysis have been demonstrated.

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Section: Discussionmentioning

confidence: 99%

Deep Learning Detection and Segmentation of Facet Joints in Ultrasound Images Based on Convolutional Neural Networks and Enhanced Data Annotation

Wu,

Xia,

Wang

et al. 2024

Diagnostics

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“…Appleharvesting robot technology is a critical factor in improving the efficiency and quality of apple production and addressing labor shortages in orchards. It holds paramount importance in reducing labor costs, alleviating labor shortages in orchards, and enhancing the economic benefits for fruit farmers [2]. Guided by intelligent technology, smart farming promotes the automation of farming production, such as automatic irrigation, fertilization, and harvesting, greatly reducing the labor intensity of farmers.…”

Section: Introductionmentioning

confidence: 99%

“…For any feature map F (S,S,C1), the sub-feature map sequence is obtained by dividing it in the following way: Typically, the feature map F is down-sampled according to a factor of scale and is partitioned from the original feature map F (S,S,C1) into sub-feature maps F ( s scale , s scale , C1) with the number of sub-feature maps scale 2 . Then, sub-feature maps F ( s scale , s scale , C1) are spliced along the channel as F ( s scale , s scale , scale 2 C1).…”

mentioning

confidence: 99%

YOLOv5-ACS: Improved Model for Apple Detection and Positioning in Apple Forests in Complex Scenes

Liu,

Wang,

Xing

2023

Forests

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Apple orchards, as an important center of economic activity in forestry special crops, can achieve yield prediction and automated harvesting by detecting and locating apples. Small apples, occlusion, dim lighting at night, blurriness, cluttered backgrounds, and other complex scenes significantly affect the automatic harvesting and yield estimation of apples. To address these issues, this study proposes an apple detection algorithm, “YOLOv5-ACS (Apple in Complex Scenes)”, based on YOLOv5s. Firstly, the space-to-depth-conv module is introduced to avoid information loss, and a squeeze-and-excitation block is added in C3 to learn more important information. Secondly, the context augmentation module is incorporated to enrich the context information of the feature pyramid network. By combining the shallow features of the backbone P2, the low-level features of the object are retained. Finally, the addition of the context aggregation block and CoordConv aggregates the spatial context pixel by pixel, perceives the spatial information of the feature map, and enhances the semantic information and global perceptual ability of the object. We conducted comparative tests in various complex scenarios and validated the robustness of YOLOv5-ACS. The method achieved 98.3% and 74.3% for mAP@0.5 and mAP@0.5:0.95, respectively, demonstrating excellent detection capabilities. This paper creates a complex scene dataset of apples on trees and designs an improved model, which can provide accurate recognition and positioning for automatic harvesting robots to improve production efficiency.

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“…Although LeNet improves the accuracy and speed of kiwifruit recognition, it does not address the issues of false or missed recognitions due to branch and leaf occlusion or overlapping. Yue et al (2019) studied apple detection by adding a boundary-weighted loss function to an improved Mask RCNN network, which led to more accurate boundary detection results. However, the network was tested under natural conditions without considering the small target problem.…”

Section: Introductionmentioning

confidence: 99%

Research on multi-cluster green persimmon detection method based on improved Faster RCNN

et al. 2023

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To address the problem of accurate recognition and localization of multiple clusters of green persimmons with similar color to the background under natural environment, this study proposes a multi-cluster green persimmon identification method based on improved Faster RCNN was proposed by using the self-built green persimmon dataset. The feature extractor DetNet is used as the backbone feature extraction network, and the model detection attention is focused on the target object itself by adding the weighted ECA channel attention mechanism to the three effective feature layers in the backbone, and the detection accuracy of the algorithm is improved. By maximizing the pooling of the lower layer features with the added attention mechanism, the high and low dimensions and magnitudes are made the same. The processed feature layers are combined with multi-scale features using a serial layer-hopping connection structure to enhance the robustness of feature information, effectively copes with the problem of target detection of objects with obscured near scenery in complex environments and accelerates the detection speed through feature complementarity between different feature layers. In this study, the K-means clustering algorithm is used to group and anchor the bounding boxes so that they converge to the actual bounding boxes, The average mean accuracy (mAP) of the improved Faster RCNN model reaches 98.4%, which was 11.8% higher than that of traditional Faster RCNN model, which also increases the accuracy of object detection during regression prediction. and the average detection time of a single image is improved by 0.54s. The algorithm is significantly improved in terms of accuracy and speed, which provides a basis for green fruit growth state monitoring and intelligent yield estimation in real scenarios.

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Detection and Classification of Bronchiectasis Based on Improved Mask-RCNN

Cited by 4 publications

References 23 publications

Deep Learning Detection and Segmentation of Facet Joints in Ultrasound Images Based on Convolutional Neural Networks and Enhanced Data Annotation

Deep Learning Detection and Segmentation of Facet Joints in Ultrasound Images Based on Convolutional Neural Networks and Enhanced Data Annotation

YOLOv5-ACS: Improved Model for Apple Detection and Positioning in Apple Forests in Complex Scenes

Research on multi-cluster green persimmon detection method based on improved Faster RCNN

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