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.

scholarly journals Hybrid Deep Neural Network for Handling Data Imbalance in Precursor MicroRNA

2021 ◽  
Vol 9 ◽  
Author(s):  
Elakkiya R. ◽  
Deepak Kumar Jain ◽  
Ketan Kotecha ◽  
Sharnil Pandya ◽  
Sai Siddhartha Reddy ◽  
...  
Keyword(s):  
Neural Network ◽  
Decision Tree ◽  
Deep Neural Network ◽  
Imbalanced Data ◽  
Vital Role ◽  
Biological Data ◽  
Decision Tree Classifier ◽  
Genome Data ◽  
Tree Classifier

Over the last decade, the field of bioinformatics has been increasing rapidly. Robust bioinformatics tools are going to play a vital role in future progress. Scientists working in the field of bioinformatics conduct a large number of researches to extract knowledge from the biological data available. Several bioinformatics issues have evolved as a result of the creation of massive amounts of unbalanced data. The classification of precursor microRNA (pre miRNA) from the imbalanced RNA genome data is one such problem. The examinations proved that pre miRNAs (precursor microRNAs) could serve as oncogene or tumor suppressors in various cancer types. This paper introduces a Hybrid Deep Neural Network framework (H-DNN) for the classification of pre miRNA in imbalanced data. The proposed H-DNN framework is an integration of Deep Artificial Neural Networks (Deep ANN) and Deep Decision Tree Classifiers. The Deep ANN in the proposed H-DNN helps to extract the meaningful features and the Deep Decision Tree Classifier helps to classify the pre miRNA accurately. Experimentation of H-DNN was done with genomes of animals, plants, humans, and Arabidopsis with an imbalance ratio up to 1:5000 and virus with a ratio of 1:400. Experimental results showed an accuracy of more than 99% in all the cases and the time complexity of the proposed H-DNN is also very less when compared with the other existing approaches.

scholarly journals Feature Extraction Techniques for Leukocyte Classification - A Review

2018 ◽  
Vol 7 (2.24) ◽  
pp. 155 ◽  
Author(s):  
Diana Baby ◽  
Sujitha Juliet Devaraj
Keyword(s):  
Neural Network ◽  
Feature Extraction ◽  
Blood Sample ◽  
Early Identification ◽  
Statistical Estimation ◽  
Vital Role ◽  
Extraction Techniques ◽  
Manual Estimation

This paper covers an investigation on the various feature extraction techniques employed for the statistical estimation of leukocyte classification from blood sample images since the identification or analysis of these four classes of leukocytes plays a vital role in the early identification of various diseases. The manual estimation of these WBC’s by pathologist is error prone and time consuming. This paper mainly concentrates on the study of leukocyte classification methodology and various feature extraction techniques for the classification of four classes of Leukocytes such as Neutrophil, Lymphocyte, Monocyte, and Eosinophil which can be fed to SVM or neural network for further classification. 

Deep Neural Network Classification of I-123 Ioflupane SPECT

2020 ◽  
Author(s):  
David T. Wang ◽  
Brady Williamson ◽  
Thomas Eluvathingal ◽  
Bruce Mahoney ◽  
Jennifer Scheler
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Neural Network Classification
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