scholarly journals A Novel System for Functional Determination of Variants of Uncertain Significance using Deep Convolutional Neural Networks

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Lior Zimmerman ◽  
Ori Zelichov ◽  
Arie Aizenmann ◽  
Zohar Barbash ◽  
Michael Vidne ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Deep Convolutional Neural Networks ◽  
Variants Of Uncertain Significance ◽  
Uncertain Significance

Determination of bone age using deep convolutional neural networks

2021 ◽  
Vol 28 (6) ◽  
pp. 1381
Author(s):  
Fatih Senel ◽  
Ahmet Dursun ◽  
Kenan Ozturk ◽  
Veysel Ayyildiz
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Bone Age ◽  
Deep Convolutional Neural Networks

scholarly journals Deep Convolutional Neural Networks models for the binary morphological classification of SDSS-galaxies

2020 ◽  
pp. 354-358
Author(s):  
M. Vasylenko ◽  
D. Dobrycheva ◽  
V. Khramtsov ◽  
I. Vavilova
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Data Augmentation ◽  
Training Sample ◽  
Learning Approach ◽  
Morphological Classification ◽  
Deep Convolutional Neural Networks ◽  
Neural Network Classifiers

We present the deep learning approach for the determination of morphological types of galaxies. We demonstrate the method's performance with the redshift-limited (z < 0.1) training sample of 6 163 galaxies from the SDSS DR9. We exploited the deep convolutional neural network classifiers such as InceptionV3, DenseNet121, and MobileNetV2 to process images of SDSS-galaxies (100x100 pixels, 25 arcsec in each axis in size) using g, r, i filters as R - G - B channels to create images. We provided the data augmentation (horizontal and vertical flips, random shifts on ±10 pixels, and rotations) randomly applied to the set of images during learning, which helped increase the classifier's generalization ability. Also, two different loss functions, MAE and Lovasz-Softmax, were applied to each classifier. The target sample galaxies were classified into two morphological types (late and early) trained on the images of galaxies from the sample. It turned out that the deep convolutional neural networks InceptionV3 and DenseNet121 with MAE-loss function show the best result attaining 93.3% accuracy.

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.

scholarly journals Deep convolutional neural networks for cardiovascular vulnerable plaque detection

2019 ◽  
Vol 277 ◽  
pp. 02024 ◽  
Author(s):  
Lincan Li ◽  
Tong Jia ◽  
Tianqi Meng ◽  
Yizhe Liu
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Vulnerable Plaque ◽  
Recall Rate ◽  
Superior Performance ◽  
Learning Approaches ◽  
Deep Convolutional Neural Networks ◽  
Vulnerable Plaques ◽  
Plaque Detection

In this paper, an accurate two-stage deep learning method is proposed to detect vulnerable plaques in ultrasonic images of cardiovascular. Firstly, a Fully Convonutional Neural Network (FCN) named U-Net is used to segment the original Intravascular Optical Coherence Tomography (IVOCT) cardiovascular images. We experiment on different threshold values to find the best threshold for removing noise and background in the original images. Secondly, a modified Faster RCNN is adopted to do precise detection. The modified Faster R-CNN utilize six-scale anchors (122,162,322,642,1282,2562) instead of the conventional one scale or three scale approaches. First, we present three problems in cardiovascular vulnerable plaque diagnosis, then we demonstrate how our method solve these problems. The proposed method in this paper apply deep convolutional neural networks to the whole diagnostic procedure. Test results show the Recall rate, Precision rate, IoU (Intersection-over-Union) rate and Total score are 0.94, 0.885, 0.913 and 0.913 respectively, higher than the 1st team of CCCV2017 Cardiovascular OCT Vulnerable Plaque Detection Challenge. AP of the designed Faster RCNN is 83.4%, higher than conventional approaches which use one-scale or three-scale anchors. These results demonstrate the superior performance of our proposed method and the power of deep learning approaches in diagnose cardiovascular vulnerable plaques.

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