EMD-DWT based transform domain feature reduction approach for quantitative multi-class classification of breast lesions

Ultrasonics ◽  
2017 ◽  
Vol 80 ◽  
pp. 22-33 ◽  
Author(s):  
Sharmin R. Ara ◽  
Syed Khairul Bashar ◽  
Farzana Alam ◽  
Md. Kamrul Hasan
Keyword(s):  
Feature Reduction ◽  
Breast Lesions ◽  
Transform Domain ◽  
Multi Class Classification ◽  
Reduction Approach

A Study on Multi Class Classification from Breast Cancer Images using Ensemble Network and Transfer Learning

2020 ◽  
Vol 14 ◽  
Author(s):  
Lahari Tipirneni ◽  
Rizwan Patan
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Convolutional Neural Network ◽  
Binary Classification ◽  
Disease Diagnosis ◽  
Feature Descriptors ◽  
Histopathological Images ◽  
Viable Approach ◽  
Multi Class Classification

Abstract:: Millions of deaths all over the world are caused by breast cancer every year. It has become the most common type of cancer in women. Early detection will help in better prognosis and increases the chance of survival. Automating the classification using Computer-Aided Diagnosis (CAD) systems can make the diagnosis less prone to errors. Multi class classification and Binary classification of breast cancer is a challenging problem. Convolutional neural network architectures extract specific feature descriptors from images, which cannot represent different types of breast cancer. This leads to false positives in classification, which is undesirable in disease diagnosis. The current paper presents an ensemble Convolutional neural network for multi class classification and Binary classification of breast cancer. The feature descriptors from each network are combined to produce the final classification. In this paper, histopathological images are taken from publicly available BreakHis dataset and classified between 8 classes. The proposed ensemble model can perform better when compared to the methods proposed in the literature. The results showed that the proposed model could be a viable approach for breast cancer classification.

Modeling and multi-class classification of vibroarthographic signals via time domain curvilinear divergence random forest

2021 ◽  
Author(s):  
Balajee Alphonse ◽  
Venkatesan Rajagopal ◽  
Sudhakar Sengan ◽  
Kousalya Kittusamy ◽  
Amudha Kandasamy ◽  
...  
Keyword(s):  
Random Forest ◽  
Time Domain ◽  
Multi Class Classification

Feature reduction for classification of multidimensional data

2000 ◽  
Vol 33 (10) ◽  
pp. 1741-1748 ◽  
Author(s):  
H. Brunzell ◽  
J. Eriksson
Keyword(s):  
Feature Reduction ◽  
Multidimensional Data
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