Instantaneous ultrasound computed tomography using deep convolutional neural networks

2021 ◽  
Author(s):  
Robert Donaldson ◽  
Jiaze He
Keyword(s):  
Computed Tomography ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks ◽  
Ultrasound Computed Tomography

scholarly journals Rib fracture detection in computed tomography images using deep convolutional neural networks

Medicine ◽  
2021 ◽  
Vol 100 (20) ◽  
pp. e26024
Author(s):  
Masafumi Kaiume ◽  
Shigeru Suzuki ◽  
Koichiro Yasaka ◽  
Haruto Sugawara ◽  
Yun Shen ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Rib Fracture ◽  
Fracture Detection ◽  
Deep Convolutional Neural Networks ◽  
Computed Tomography Images

scholarly journals Detection and diagnosis of colitis on computed tomography using deep convolutional neural networks

2017 ◽  
Vol 44 (9) ◽  
pp. 4630-4642 ◽  
Author(s):  
Jiamin Liu ◽  
David Wang ◽  
Le Lu ◽  
Zhuoshi Wei ◽  
Lauren Kim ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks ◽  
Detection And Diagnosis

CNN-based Classification of Degraded Images

2020 ◽  
Vol 2020 (10) ◽  
pp. 28-1-28-7 ◽  
Author(s):  
Kazuki Endo ◽  
Masayuki Tanaka ◽  
Masatoshi Okutomi
Keyword(s):  
Neural Networks ◽  
Image Restoration ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks ◽  
Alternative Approach ◽  
Degraded Image ◽  
Degraded Images ◽  
Straightforward Approach

Classification of degraded images is very important in practice because images are usually degraded by compression, noise, blurring, etc. Nevertheless, most of the research in image classification only focuses on clean images without any degradation. Some papers have already proposed deep convolutional neural networks composed of an image restoration network and a classification network to classify degraded images. This paper proposes an alternative approach in which we use a degraded image and an additional degradation parameter for classification. The proposed classification network has two inputs which are the degraded image and the degradation parameter. The estimation network of degradation parameters is also incorporated if degradation parameters of degraded images are unknown. The experimental results showed that the proposed method outperforms a straightforward approach where the classification network is trained with degraded images only.

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