Thigh fracture detection using deep learning method based on new dilated convolutional feature pyramid network

Guan, Bin; Yao, Jinkun; Zhang, Guoshan; Wang, Xinbo

doi:10.1016/j.patrec.2019.06.015

Cited by 55 publications

(20 citation statements)

References 11 publications

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“…For the motion characteristics of the target, the Mahalanobis distance between the predicted Kalman state and the newly generated state is collected for evaluation [ 30 ]:

…”

Section: Vehicle-road Collaborative Information Interaction System An...mentioning

confidence: 99%

Intelligent Research Based on Deep Learning Recognition Method in Vehicle-Road Cooperative Information Interaction System

Jiao¹

2022

Computational Intelligence and Neuroscience

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The vehicle-road collaborative information interaction system is an emerging technology system that realizes the sharing of information between vehicles, vehicles and roads between traffic road information, and driving vehicle information. It is of positive significance for improving the urban transportation construction system and promoting urban economic development. This paper conducts intelligent research on the deep learning recognition method based on the vehicle-road collaborative information interaction system. First, this article comprehensively expounds the concept of the vehicle-road collaborative information interaction system and then introduces the specific components, functions, and applications of the system structure. Then, this article researches on deep learning recognition methods and introduces three deep learning recognition methods. They are background extraction method, YOLOv2 method, and DeepSORT method. Finally, this paper conducts simulation comparison experiments between deep learning algorithms and traditional algorithms. It evaluates the feasibility of the algorithm in the vehicle-road collaborative information interaction system in three aspects: vehicle target detection, vehicle flow identification, and emergency decision-making. The experimental results show that the value of the intersection ratio of vehicle target detection in the deep learning recognition method is 8.66% higher than that of the traditional algorithm, the recall rate is 7% higher than that of the traditional algorithm, and the vehicle flow recognition accuracy is 1.8% higher than that of the traditional algorithm. The early warning time in emergency decision-making is also shorter than that of traditional algorithms, which shows the unique superiority and feasibility of deep learning algorithms in the vehicle-road collaborative information interaction system.

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“…For the motion characteristics of the target, the Mahalanobis distance between the predicted Kalman state and the newly generated state is collected for evaluation [ 30 ]:

…”

Section: Vehicle-road Collaborative Information Interaction System An...mentioning

confidence: 99%

Intelligent Research Based on Deep Learning Recognition Method in Vehicle-Road Cooperative Information Interaction System

Jiao¹

2022

Computational Intelligence and Neuroscience

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“…Deep learning and quantum machine learning methodologies are widely utilized for tumor localization and classification [201]. In these techniques, automatic feature learning helps to discriminate complicated patterns [186,[202][203][204][205][206][207][208][209][210][211][212][213].…”

Section: Recent Trends In Medical Imaging To Detect Malignancymentioning

confidence: 99%

Brain tumor detection and classification using machine learning: a comprehensive survey

Amin

Sharif

Haldorai³

et al. 2021

Complex Intell. Syst.

215

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Brain tumor occurs owing to uncontrolled and rapid growth of cells. If not treated at an initial phase, it may lead to death. Despite many significant efforts and promising outcomes in this domain, accurate segmentation and classification remain a challenging task. A major challenge for brain tumor detection arises from the variations in tumor location, shape, and size. The objective of this survey is to deliver a comprehensive literature on brain tumor detection through magnetic resonance imaging to help the researchers. This survey covered the anatomy of brain tumors, publicly available datasets, enhancement techniques, segmentation, feature extraction, classification, and deep learning, transfer learning and quantum machine learning for brain tumors analysis. Finally, this survey provides all important literature for the detection of brain tumors with their advantages, limitations, developments, and future trends.

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“…The average precision (AP) score obtained from the fracture detection performed by Guan et al, using a dilated convolutional feature pyramid network (DCFPN) on 3842 thigh fracture X-ray images is 82.1%. [ 3 ]. The highest result of fracture detection performed by Guan et al, on approximately 4000 arm fracture X-ray images in a musculoskeletal radiograph (MURA) dataset, 62.04% AP, was obtained using proposed two-stage region-based convolutional neural networks (R-CNN) method [ 4 ].…”

Section: Related Workmentioning

confidence: 99%

Fracture Detection in Wrist X-ray Images Using Deep Learning-Based Object Detection Models

Hardalaç

Uysal

Peker

et al. 2022

Sensors

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Hospitals, especially their emergency services, receive a high number of wrist fracture cases. For correct diagnosis and proper treatment of these, images obtained from various medical equipment must be viewed by physicians, along with the patient’s medical records and physical examination. The aim of this study is to perform fracture detection by use of deep-learning on wrist X-ray images to support physicians in the diagnosis of these fractures, particularly in the emergency services. Using SABL, RegNet, RetinaNet, PAA, Libra R-CNN, FSAF, Faster R-CNN, Dynamic R-CNN and DCN deep-learning-based object detection models with various backbones, 20 different fracture detection procedures were performed on Gazi University Hospital’s dataset of wrist X-ray images. To further improve these procedures, five different ensemble models were developed and then used to reform an ensemble model to develop a unique detection model, ‘wrist fracture detection-combo (WFD-C)’. From 26 different models for fracture detection, the highest detection result obtained was 0.8639 average precision (AP50) in the WFD-C model. Huawei Turkey R&D Center supports this study within the scope of the ongoing cooperation project coded 071813 between Gazi University, Huawei and Medskor.

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Thigh fracture detection using deep learning method based on new dilated convolutional feature pyramid network

Cited by 55 publications

References 11 publications

Intelligent Research Based on Deep Learning Recognition Method in Vehicle-Road Cooperative Information Interaction System

Intelligent Research Based on Deep Learning Recognition Method in Vehicle-Road Cooperative Information Interaction System

Brain tumor detection and classification using machine learning: a comprehensive survey

Fracture Detection in Wrist X-ray Images Using Deep Learning-Based Object Detection Models

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