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 Automatic Evaluation of the Lung Condition of COVID-19 Patients Using X-ray Images and Convolutional Neural Networks

2021 ◽  
Vol 11 (1) ◽  
pp. 28
Author(s):  
Ivan Lorencin ◽  
Sandi Baressi Šegota ◽  
Nikola Anđelić ◽  
Anđela Blagojević ◽  
Tijana Šušteršić ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Clinical Picture ◽  
Data Augmentation ◽  
Lower Amount ◽  
Training Procedure ◽  
X Ray ◽  
Severe Clinical Picture ◽  
Lung Condition

COVID-19 represents one of the greatest challenges in modern history. Its impact is most noticeable in the health care system, mostly due to the accelerated and increased influx of patients with a more severe clinical picture. These facts are increasing the pressure on health systems. For this reason, the aim is to automate the process of diagnosis and treatment. The research presented in this article conducted an examination of the possibility of classifying the clinical picture of a patient using X-ray images and convolutional neural networks. The research was conducted on the dataset of 185 images that consists of four classes. Due to a lower amount of images, a data augmentation procedure was performed. In order to define the CNN architecture with highest classification performances, multiple CNNs were designed. Results show that the best classification performances can be achieved if ResNet152 is used. This CNN has achieved AUCmacro¯ and AUCmicro¯ up to 0.94, suggesting the possibility of applying CNN to the classification of the clinical picture of COVID-19 patients using an X-ray image of the lungs. When higher layers are frozen during the training procedure, higher AUCmacro¯ and AUCmicro¯ values are achieved. If ResNet152 is utilized, AUCmacro¯ and AUCmicro¯ values up to 0.96 are achieved if all layers except the last 12 are frozen during the training procedure.

Sign in / Sign up

Export Citation Format

Share Document