Modified YOLOv4-DenseNet Algorithm for Detection of Ventricular Septal Defects in Ultrasound Images

Chen, Shih-Hsin; Wang, Chunwei; Tai, I-Hsin; Weng, Ken‐Pen; Chen, Yihui; Hsieh, Kai‐Sheng

doi:10.9781/ijimai.2021.06.001

Cited by 5 publications

(5 citation statements)

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“…The method's feasibility and ease of adoption may change based on the resources and skills available in different circumstances. Finally, the suggested technique's real-time object identification and recognition capabilities necessitate strong hardware [59]. This may limit its scalability and usability in resource-constrained contexts when access to such technology is limited or prohibitively expensive.…”

Section: Discussionmentioning

confidence: 99%

“…It is critical to have strong data privacy and security procedures in place to handle privacy issues and safeguard user confidentiality. So many researchers are researching on the related topics [59][60][61][62]. The study does not go into detail about the privacy precautions that were put in place, and more research is needed to analyze the privacy consequences of the proposed approach.…”

Section: Discussionmentioning

confidence: 99%

See 1 more Smart Citation

YOLO and Blockchain Technology Applied to Intelligent Transportation License Plate Character Recognition for Security

Alharbi,

Alshahrani,

Zakariah

et al. 2023

CMC

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Privacy and trust are significant issues in intelligent transportation systems (ITS). Data security is critical in ITS systems since sensitive user data is communicated to another user over the internet through wireless devices and routes such as radio channels, optical fiber, and blockchain technology. The Internet of Things (IoT) is a network of connected, interconnected gadgets. Privacy issues occasionally arise due to the amount of data generated. However, they have been primarily addressed by blockchain and smart contract technology. While there are still security issues with smart contracts, primarily due to the complexity of writing the code, there are still many challenges to consider when designing blockchain designs for the IoT environment. This study uses traditional blockchain technology with the "You Only Look Once" (YOLO) object detection method to accurately locate and identify license plates. While YOLO and blockchain technologies used for intelligent vehicle license plate recognition are promising, they have received limited research attention. Real-time object identification and recognition would be possible by combining a cutting-edge object detection technique with a regional convolutional neural network (RCNN) built with the tensor flow core open source libraries. This method works reasonably well for identifying any license plate. The Automatic License Plate Recognition (ALPR) approach delivered outstanding results in various datasets. First, with a recognition rate of 96.2%, our system (UFPR-ALPR) surpassed the previously used technology, consisting of 4500 frames and around 150 films. Second, a deep learning algorithm was trained to recognize images of license plate numbers using the UFPR-ALPR dataset. Third, the license plate's characters were complicated for standard methods to identify because of the shifting lighting correctly. The proposed model, however, produced beneficial outcomes. KEYWORDS Intelligent transportation system; blockchain technology; license plate recognition; privacy; YOLO; deep learning technique; ALPRAdditionally, noise removal techniques such as Gaussian blur and median filtering were applied to remove any noise or artifacts in the license plate images. Finally, the pre-processed dataset was split into training, and testing sets with a ratio of 80:20 to evaluate the model's performance on unseen data. These pre-processing techniques ensure that the license plate recognition model is trained on a high-quality dataset, leading to improved accuracy in the recognition process. Materials and MethodsInformation about traffic management, environmental changes, and congestion is gathered and disseminated through a transportation system known as ITS. Communications between vehicles and between vehicles and infrastructure are made possible by ITS in various ways. In addition, ITS includes information from both wired and wireless connections. Infrastructure-to-infrastructure communication typically uses cable technology, while V2V and V2I communication frequently use wireles...

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

YOLO and Blockchain Technology Applied to Intelligent Transportation License Plate Character Recognition for Security

Alharbi,

Alshahrani,

Zakariah

et al. 2023

CMC

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“…30 In 2021, Chen et al modified the single-stage detector YOLOv4 to recognize the ventricular septal defect in echocardiographic images. 31 Bassiouny et al compared Faster R-CNN and RetinaNet in discriminate lung ultrasound feature and the precision of Faster R-CNN is superior to the RetinaNet. 32 Besides, Faster R-CNN and YOLOv5 models were also applied in detecting breast nodules and recognizing fetal anatomical plane.…”

Section: Recently the Application Of DL Object Detection Technology I...mentioning

confidence: 99%

“…30 (e) Ventricular septal defect in echocardiographic image. 31 (f) Lung ultrasound feature. 32 (g) Anatomical plane in fetus.…”

Section: Recently the Application Of DL Object Detection Technology I...mentioning

confidence: 99%

Deep-learning-based sampling position selection on color doppler sonography images during renal artery ultrasound scanning

Wang

Yang

Cai

et al. 2023

Preprint

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Background Appropriate sampling position selection is a key step of the renal artery ultrasound examination to obtain proper spectral waveform to evaluate the renal artery blood flow, which is a challenge for inexperience physicians. Based on deep learning (DL) technology, this study models sampling position selection as an object detection process in the color doppler sonography (CDS) images to assist renal artery ultrasound scanning.Methods 2004 patients received renal artery ultrasound examination in Peking Union Medical College Hospital from August 2017 to December 2019 were included. CDS images from these patients were classified into four categories, abdominal aorta (AO), normal renal artery (NRA), renal artery stenosis (RAS), and intrarenal interlobular artery (IRA) according to scanning position, and then randomly split into model training dataset (N = 6661 images), parameter optimizing dataset (N = 441), and clinical validation dataset (N = 1243). Seven DL object detection models, including three two-stage models (Faster R-CNN, Cascade R-CNN, and Double Head R-CNN), and four one-stage models (RetinaNet, YOLOv3, FoveaBox, and Deformable DETR), were trained and evaluated. The predictive accuracy of sampling position selection was calculated as an indicator of model’s efficiency. For each model, 10 trained results were obtained and the difference of seven models’ efficiencies were compared with independent two-sample t-test.Results The Double Head R-CNN model achieved the significantly higher average accuracies on both parameter optimizing and validation datasets (89.3 ± 0.6% and 88.5 ± 0.3%) than other methods (P-value < 0.001). Performance of three two-stage DL object detection models were better than the RetinaNet, FoveaBox, and Deformable DETR (P < 0.001). On clinical validation data, predictive accuracies of the Double Head R-CNN model on four types of images (AO, NRA, RAS, and IRA) were 86.5 ± 1.1%, 90.4 ± 0.1%, 84.7 ± 1.0%, and 88.8 ± 0.6% respectively, which were all significantly higher than the other methods (P < 0.001). Besides the predictive performance of Double Head R-CNN model on NRA and IRA images were better than that on the RAS and AO (P < 0.001).Conclusions The DL object detection model achieves well predictive validity and is promising to help physicians to improve the accuracy of sampling position selection during renal artery ultrasound examination.

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“…For the comprehensiveness of the results, we eventually selected CenterNet [34], YOLOv3 [35], YOLOv4-tiny [36], YOLOv4 [37] and YOLOv5 [38], as our testing algorithms for building a benchmark on STAR-24K. These baseline models cover the popular and the state-of-the-art deep learning target detection networks [39] [40] [41], thus can offer valuable benchmark results for dataset evaluation. Meanwhile, to demonstrate the effectiveness of data augmentation, we add a set of comparative experiments.…”

Section: Evaluationsmentioning

confidence: 99%

STAR-24K: A Public Dataset for Space Common Target Detection

2022

KSII TIIS

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The target detection algorithm based on supervised learning is the current mainstream algorithm for target detection. A high-quality dataset is the prerequisite for the target detection algorithm to obtain good detection performance. The larger the number and quality of the dataset, the stronger the generalization ability of the model, that is, the dataset determines the upper limit of the model learning. The convolutional neural network optimizes the network parameters in a strong supervision method. The error is calculated by comparing the predicted frame with the manually labeled real frame, and then the error is passed into the network for continuous optimization. Strongly supervised learning mainly relies on a large number of images as models for continuous learning, so the number and quality of images directly affect the results of learning. This paper proposes a dataset STAR-24K (meaning a dataset for Space TArget Recognition with more than 24,000 images) for detecting common targets in space. Since there is currently no publicly available dataset for space target detection, we extracted some pictures from a series of channels such as pictures and videos released by the official websites of NASA (National Aeronautics and Space Administration) and ESA (The European Space Agency) and expanded them to 24,451 pictures. We evaluate popular object detection algorithms to build a benchmark. Our STAR-24K dataset is publicly available at https://github.com/Zzz-zcy/STAR-24K.

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Modified YOLOv4-DenseNet Algorithm for Detection of Ventricular Septal Defects in Ultrasound Images

Cited by 5 publications

References 16 publications

YOLO and Blockchain Technology Applied to Intelligent Transportation License Plate Character Recognition for Security

YOLO and Blockchain Technology Applied to Intelligent Transportation License Plate Character Recognition for Security

Deep-learning-based sampling position selection on color doppler sonography images during renal artery ultrasound scanning

STAR-24K: A Public Dataset for Space Common Target Detection

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