Deep learning object detection of maxillary cyst-like lesions on panoramic radiographs: preliminary study

2020 ◽  
Author(s):  
Hirofumi Watanabe ◽  
Yoshiko Ariji ◽  
Motoki Fukuda ◽  
Chiaki Kuwada ◽  
Yoshitaka Kise ◽  
...  
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Learning Object ◽  
Panoramic Radiographs ◽  
Maxillary Cyst ◽  
Preliminary Study

scholarly journals Performance of deep learning object detection technology in the detection and diagnosis of maxillary sinus lesions on panoramic radiographs

2020 ◽  
pp. 20200171 ◽  
Author(s):  
Ryosuke Kuwana ◽  
Yoshiko Ariji ◽  
Motoki Fukuda ◽  
Yoshitaka Kise ◽  
Michihito Nozawa ◽  
...  
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Maxillary Sinus ◽  
False Positive ◽  
Maxillary Sinusitis ◽  
Learning Object ◽  
Data Sets ◽  
Panoramic Radiographs ◽  
Class 1 ◽  
Maxillary Sinuses

Objective: The first aim of this study was to determine the performance of a deep learning object detection technique in the detection of maxillary sinuses on panoramic radiographs. The second aim was to clarify the performance in the classification of maxillary sinus lesions compared with healthy maxillary sinuses. Methods: The imaging data for healthy maxillary sinuses (587 sinuses, Class 0), inflamed maxillary sinuses (416 sinuses, Class 1), cysts of maxillary sinus regions (171 sinuses, Class 2) were assigned to training, testing 1, and testing 2 data sets. A learning process of 1000 epochs with the training images and labels was performed using DetectNet, and a learning model was created. The testing 1 and testing 2 images were applied to the model, and the detection sensitivities and the false-positive rates per image were calculated. The accuracies, sensitivities and specificities were determined for distinguishing the inflammation group (Class 1) and cyst group (Class 2) with respect to the healthy group (Class 0). Results: Detection sensitivities of healthy (Class 0) and inflamed (Class 1) maxillary sinuses were 100% for both testing 1 and testing 2 data sets, whereas they were 98 and 89% for cysts of the maxillary sinus regions (Class 2). False-positive rates per image were nearly 0.00. Accuracies, sensitivities and specificities for diagnosis maxillary sinusitis were 90–91%, 88–85%, and 91–96%, respectively; for cysts of the maxillary sinus regions, these values were 97–100%, 80–100%, and 100–100%, respectively. Conclusion: Deep learning could reliably detect the maxillary sinuses and identify maxillary sinusitis and cysts of the maxillary sinus regions. Advances in knowledge: This study using a deep leaning object detection technique indicated that the detection sensitivities of maxillary sinuses were high and the performance of maxillary sinus lesion identification was ≧80%. In particular, performance of sinusitis identification was ≧90%.

Experimental Deep Learning Object Detection in Real-time Colonoscopies

2021 ◽  
Author(s):  
Adrian Ciobanu ◽  
Mihaela Luca ◽  
Tudor Barbu ◽  
Vasile Drug ◽  
Andrei Olteanu ◽  
...  
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Real Time ◽  
Learning Object

scholarly journals An Embedded Deep Learning Object Detection Model For Traffic In Asian Countries

2020 ◽  
Author(s):  
Weiju Chen ◽  
WanChen Wu ◽  
Hao-Wei Chang ◽  
Wei-Liang Lin ◽  
Changhua Yang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Learning Object ◽  
Asian Countries ◽  
Detection Model

scholarly journals A Mobile Outdoor Augmented Reality Method Combining Deep Learning Object Detection and Spatial Relationships for Geovisualization

Sensors ◽  
2017 ◽  
Vol 17 (9) ◽  
pp. 1951 ◽  
Author(s):  
Jinmeng Rao ◽  
Yanjun Qiao ◽  
Fu Ren ◽  
Junxing Wang ◽  
Qingyun Du
Keyword(s):  
Deep Learning ◽  
Augmented Reality ◽  
Object Detection ◽  
Learning Object ◽  
Spatial Relationships ◽  
Outdoor Augmented Reality

scholarly journals Deep Learning Object Detector Using a Combination of Convolutional Neural Network (CNN) Architecture (MiniVGGNet) and Classic Object Detection Algorithm

2020 ◽  
Vol 28 (S2) ◽  
Author(s):  
Asmida Ismail ◽  
Siti Anom Ahmad ◽  
Azura Che Soh ◽  
Mohd Khair Hassan ◽  
Hazreen Haizi Harith
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Object Detection ◽  
Convolutional Neural Network ◽  
Detection System ◽  
Object Classification ◽  
Detection Algorithm ◽  
Learning Object ◽  
Sliding Windows ◽  
Detection Algorithms

The object detection system is a computer technology related to image processing and computer vision that detects instances of semantic objects of a certain class in digital images and videos. The system consists of two main processes, which are classification and detection. Once an object instance has been classified and detected, it is possible to obtain further information, including recognizes the specific instance, track the object over an image sequence and extract further information about the object and the scene. This paper presented an analysis performance of deep learning object detector by combining a deep learning Convolutional Neural Network (CNN) for object classification and applies classic object detection algorithms to devise our own deep learning object detector. MiniVGGNet is an architecture network used to train an object classification, and the data used for this purpose was collected from specific indoor environment building. For object detection, sliding windows and image pyramids were used to localize and detect objects at different locations, and non-maxima suppression (NMS) was used to obtain the final bounding box to localize the object location. Based on the experiment result, the percentage of classification accuracy of the network is 80% to 90% and the time for the system to detect the object is less than 15sec/frame. Experimental results show that there are reasonable and efficient to combine classic object detection method with a deep learning classification approach. The performance of this method can work in some specific use cases and effectively solving the problem of the inaccurate classification and detection of typical features.

scholarly journals Deep-learning Object Detection for Resource Recycling

2020 ◽  
Vol 1583 ◽  
pp. 012011
Author(s):  
Yeong-Lin Lai ◽  
Yeong-Kang Lai ◽  
Syuan-Yu Shih ◽  
Chun-Yi Zheng ◽  
Ting-Hsueh Chuang
Keyword(s):  
Deep Learning ◽  
Object Detection ◽  
Learning Object ◽  
Resource Recycling
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