Comparison of deep convolutional neural networks for classification of breast ultrasound images

2021 ◽  
Vol 70 (1) ◽  
pp. 176-183
Author(s):  
Juyoung Park ◽  
Yisak Kim ◽  
Chang-Wan Ryu ◽  
Hyungsuk Kim
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Breast Ultrasound ◽  
Ultrasound Images ◽  
Deep Convolutional Neural Networks

scholarly journals Hybrid classification of diffuse liver diseases in ultrasound images using deep convolutional neural networks

2021 ◽  
Vol 22 ◽  
pp. 100496
Author(s):  
Pezhman Pasyar ◽  
Tahereh Mahmoudi ◽  
Seyedeh-Zahra Mousavi Kouzehkanan ◽  
Alireza Ahmadian ◽  
Hossein Arabalibeik ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Liver Diseases ◽  
Ultrasound Images ◽  
Deep Convolutional Neural Networks ◽  
Hybrid Classification ◽  
Diffuse Liver Diseases

Classification of Breast Ultrasound Images Based on Convolutional Neural Networks - A Comparative Study

2021 ◽  
Author(s):  
Samir M. Badawy ◽  
Abd El-Naser A. Mohamed ◽  
Alaa A. Hefnawy ◽  
Hassan E. Zidan ◽  
Mohammed T. GadAllah ◽  
...  
Keyword(s):  
Neural Networks ◽  
Comparative Study ◽  
Convolutional Neural Networks ◽  
Breast Ultrasound ◽  
Ultrasound Images

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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