Computer-assisted frameworks for classification of liver, breast and blood neoplasias via neural networks: A survey based on medical images

2019 ◽  
Vol 335 ◽  
pp. 274-298 ◽  
Author(s):  
Antonio Brunetti ◽  
Leonarda Carnimeo ◽  
Gianpaolo Francesco Trotta ◽  
Vitoantonio Bevilacqua
Keyword(s):  
Neural Networks ◽  
Medical Images ◽  
Computer Assisted

scholarly journals CONVOLUTIONAL NEURAL NETWORKS IN THE PROBLEM OF CLASSIFICATION OF MEDICAL IMAGES

2021 ◽  
pp. 68-73
Author(s):  
L.I. Zelenina ◽  
L.E. Khaymina ◽  
E.A. Demenkova ◽  
M.E. Demenkov ◽  
E.S. Khaymin ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Medical Images

Implementation of Wavelets and Artificial Neural Networks in Colonic Histopathological Classification

2014 ◽  
Vol 18 (5) ◽  
pp. 792-797 ◽  
Author(s):  
Samantha Denise F. Hilado ◽  
◽  
Laurence A. Gan Lim ◽  
Raouf N. G. Naguib ◽  
Elmer P. Dadios ◽  
...  
Keyword(s):  
Neural Networks ◽  
Colon Cancer ◽  
Death Rate ◽  
Computer Assisted ◽  
Histopathological Classification ◽  
High Death Rate ◽  
High Death ◽  
Histopathological Images ◽  
Assisted Diagnosis

Colon cancer is one type of cancer that has a high death rate, but early diagnosis can improve the chances of patient recovery. Computer-assisted diagnosis can aid in determining whether images are of healthy or cancerous tissues. This study aims to contribute to the automatic classification of microscopic colonic images by implementing a 2-D wavelet transform for feature extraction and neural networks for classification. The colonic histopathological images are assigned to either the normal, cancerous, or adenomatous polyp classes. The proposed algorithm is able to determine which of the three classes the images belong to at a 91.11% rate of 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.

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