scholarly journals Multipatch-GLCM for Texture Feature Extraction on Classification of the Colon Histopathology Images using Deep Neural Network with GPU Acceleration

2020 ◽  
Vol 16 (3) ◽  
pp. 280-294 ◽  
Author(s):  
Toto Haryanto ◽  
Adib Pratama ◽  
Heru Suhartanto ◽  
Aniati Murni ◽  
Kusmardi Kusmardi ◽  
...  
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Deep Neural Network ◽  
Texture Feature ◽  
Gpu Acceleration ◽  
Texture Feature Extraction ◽  
Histopathology Images

Deep learning neural network for texture feature extraction in oral cancer: enhanced loss function

2020 ◽  
Vol 79 (37-38) ◽  
pp. 27867-27890 ◽  
Author(s):  
Bishal Bhandari ◽  
Abeer Alsadoon ◽  
P. W. C. Prasad ◽  
Salma Abdullah ◽  
Sami Haddad
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Deep Learning ◽  
Oral Cancer ◽  
Loss Function ◽  
Texture Feature ◽  
Texture Feature Extraction ◽  
Deep Learning Neural Network

scholarly journals Breast Cancer Detection Using Combination of Feature Extraction Models

2018 ◽  
Vol 7 (3.12) ◽  
pp. 848
Author(s):  
T Suneetha Rani ◽  
S J Soujanya ◽  
Pole Anjaiah
Keyword(s):  
Breast Cancer ◽  
Feature Extraction ◽  
Cancer Detection ◽  
Texture Feature ◽  
Region Of Interest ◽  
Computation Time ◽  
Morphological Operations ◽  
Texture Feature Extraction ◽  
Mammogram Images

Recognition of either masses or tissues in a mammogram digital images is a key issue for radiologist. Present methods uses medial filter and morphological operations for detection of suspected cases in a mammogram. They use region of interest (ROI) segmentation for extraction of masses and classification of levels of severities.  Classification of large number of mammogram images based on breast cancer cases takes longer computation time for performing of ROI segmentation.  This is addressed by multi-ROI segmentation and it retrieves the textual properties of large mammogram images for effectively determining the breast cancer mammogram images.Experimental results shows the better performance of proposed method than existing ROI based texture feature extraction.

scholarly journals Robust Iris Classification Based on Deep Neural Network (DNN) and Stationary Wavelet Transform (SWT)

2019 ◽  
pp. 198-204
Author(s):  
Priyanka S ◽  
Pavithra V ◽  
Pavithra M ◽  
S. Bhuvana
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Wavelet Transform ◽  
Major Part ◽  
Deep Neural Network ◽  
Local Binary Pattern ◽  
Vital Role ◽  
Stationary Wavelet Transform ◽  
Huge Amount

The eye is a vital part of our body. It consists of several layers like sclera, retina, tunica, and iris. Among these several layers, Iris plays a vital role in human visionary. There are various infections which affect the Iris functioning. The sign, symptoms, and diagnosis of this is still a challenge for doctors. To overcome this many techniques and technologies have been introduced. But still, the existing system has several drawbacks in recognition like a huge amount of dataset, classification, extraction, etc. To overcome this we propose a system where Deep Neural Network plays a major part. It classifies the iris disease in our eyes in a more clear and precise manner. In additional to Deep Neural Network several other algorithms have been used like Stationary Wavelet Transform, for image selection and recognition, Local Binary Pattern, for Feature extraction and at a final stage Deep Neural Network for classification of Iris images.

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