Deep learning with a convolutional neural network algorithm for automated detection of urinary tract stones using abdominal X-ray image

2019 ◽  
Vol 18 (1) ◽  
pp. e1077-e1078 ◽  
Author(s):  
J. Ishioka ◽  
M. Kobayashi ◽  
M. Fujiwara ◽  
N. Kawamura ◽  
T. Okuno ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Urinary Tract ◽  
Convolutional Neural Network ◽  
Automated Detection ◽  
X Ray ◽  
Network Algorithm ◽  
Urinary Tract Stones ◽  
Neural Network Algorithm

Computer-aided Diagnosis with a Convolutional Neural Network Algorithm for Automated Detection of Urinary Tract Stones on Plain X-ray

2021 ◽  
Author(s):  
Masaki Kobayashi ◽  
Junichiro Ishioka ◽  
Yoh Matsuoka ◽  
Yuichi Fukuda ◽  
Yusuke Kohno ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Urinary Tract ◽  
Convolutional Neural Network ◽  
Computer Aided Diagnosis ◽  
Cad Model ◽  
X Ray ◽  
Computer Aided ◽  
Urinary Tract Stones ◽  
Aided Diagnosis

Abstract Background: Recent increased use of medical images induces further burden of their interpretation for physicians. A plain X-ray is a low-cost examination that has low-dose radiation exposure and high availability, although diagnosing urolithiasis using this method is not always easy. Since the advent of a convolutional neural network via deep learning in the 2000s, computer-aided diagnosis (CAD) has had a great impact on automatic image analysis in the urological field. The objective of our study was to develop a CAD system with deep learning architecture to detect urinary tract stones on a plain X-ray and to evaluate the model’s accuracy. Methods: We collected plain X-ray images of 1017 patients with a radio-opaque urinary tract stone. X-ray images (n=827 and 190) were used as the training and test data, respectively. We used a 17-layer Residual Network as a convolutional neural network architecture for patch-wise training. The training data were repeatedly used until the best model accuracy was achieved within 300 runs. The F score, which is a harmonic mean of the sensitivity and positive predictive value (PPV) and represents the balance of the accuracy, was measured to evaluate the model’s accuracy. Results: Using deep learning, we developed a CAD model that needed 110 ms to provide an answer for each X-ray image. The best F score was 0.752, and the sensitivity and PPV were 0.872 and 0.662, respectively. When limited to a proximal ureter stone, the sensitivity and PPV were 0.925 and 0.876, respectively, and they were the lowest at mid-ureter. Conclusion: CAD of a plain X-ray may be a promising method to detect radio-opaque urinary tract stones with satisfactory sensitivity although the PPV could still be improved. The CAD model detects urinary tract stones quickly and automatically and has the potential to become a helpful screening modality especially for primary care physicians for diagnosing urolithiasis. Further study using a higher volume of data would improve the diagnostic performance of CAD models to detect urinary tract stones on a plain X-ray.

scholarly journals Computer-aided diagnosis with a convolutional neural network algorithm for automated detection of urinary tract stones on plain X-ray

BMC Urology ◽  
2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Masaki Kobayashi ◽  
Junichiro Ishioka ◽  
Yoh Matsuoka ◽  
Yuichi Fukuda ◽  
Yusuke Kohno ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Urinary Tract ◽  
Convolutional Neural Network ◽  
Computer Aided Diagnosis ◽  
Cad Model ◽  
X Ray ◽  
Computer Aided ◽  
Urinary Tract Stones ◽  
Aided Diagnosis

Abstract Background Recent increased use of medical images induces further burden of their interpretation for physicians. A plain X-ray is a low-cost examination that has low-dose radiation exposure and high availability, although diagnosing urolithiasis using this method is not always easy. Since the advent of a convolutional neural network via deep learning in the 2000s, computer-aided diagnosis (CAD) has had a great impact on automatic image analysis in the urological field. The objective of our study was to develop a CAD system with deep learning architecture to detect urinary tract stones on a plain X-ray and to evaluate the model’s accuracy. Methods We collected plain X-ray images of 1017 patients with a radio-opaque upper urinary tract stone. X-ray images (n = 827 and 190) were used as the training and test data, respectively. We used a 17-layer Residual Network as a convolutional neural network architecture for patch-wise training. The training data were repeatedly used until the best model accuracy was achieved within 300 runs. The F score, which is a harmonic mean of the sensitivity and positive predictive value (PPV) and represents the balance of the accuracy, was measured to evaluate the model’s accuracy. Results Using deep learning, we developed a CAD model that needed 110 ms to provide an answer for each X-ray image. The best F score was 0.752, and the sensitivity and PPV were 0.872 and 0.662, respectively. When limited to a proximal ureter stone, the sensitivity and PPV were 0.925 and 0.876, respectively, and they were the lowest at mid-ureter. Conclusion CAD of a plain X-ray may be a promising method to detect radio-opaque urinary tract stones with satisfactory sensitivity although the PPV could still be improved. The CAD model detects urinary tract stones quickly and automatically and has the potential to become a helpful screening modality especially for primary care physicians for diagnosing urolithiasis. Further study using a higher volume of data would improve the diagnostic performance of CAD models to detect urinary tract stones on a plain X-ray.

scholarly journals MFDNN: Multi-channel feature deep neural network algorithm to identify Covid19 chest X-ray images

2021 ◽  
Author(s):  
Liangrui Pan ◽  
boya ji ◽  
Xiaoqi wang ◽  
shaoliang peng
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Deep Neural Network ◽  
Test Accuracy ◽  
Learning Networks ◽  
X Ray ◽  
Network Algorithm ◽  
Life Saving ◽  
Chest X Ray ◽  
Neural Network Algorithm

The use of chest X-ray images (CXI) to detect Severe Acute Respiratory Syndrome Coronavirus 2 (SARS CoV-2) caused by Coronavirus Disease 2019 (COVID-19) is life-saving important for both patients and doctors. This research proposed a multi-channel feature deep neural network algorithm to screen people infected with COVID-19. The algorithm integrates data oversampling technology and a multi-channel feature deep neural network model to carry out the training process in an end-to-end manner. In the experiment, we used a publicly available CXI database with 10,192 Normal, 6012 Lung Opacity (Non-COVID lung infection), and 1345 Viral Pneumonia images. Compared with traditional deep learning models (Densenet201, ResNet50, VGG19, GoogLeNet), the MFDNN model obtains an average test accuracy of 93.19% in all data. Furthermore, in each type of screening, the precision, recall, and F1 Score of the MFDNN model are also better than traditional deep learning networks. Secondly, compared with the latest CoroDet model, the MFDNN algorithm is 1.91% higher than the CoroDet model in the experiment of detecting the four categories of COVID19 infected persons. Finally, our experimental code will be placed at https://github.com/panliangrui/covid19.

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