scholarly journals Classification of breast lesions in ultrasonography using sparse logistic regression and morphology-based texture features

2018 ◽  
Vol 45 (9) ◽  
pp. 4112-4124 ◽  
Author(s):  
Hoda Nemat ◽  
Hamid Fehri ◽  
Nasrin Ahmadinejad ◽  
Alejandro F. Frangi ◽  
Ali Gooya
Keyword(s):  
Logistic Regression ◽  
Texture Features ◽  
Breast Lesions ◽  
Sparse Logistic Regression

scholarly journals Study of classification of breast lesions using texture GLCM features obtained from the raw ultrasound signal

2020 ◽  
Author(s):  
Mariusz Nieniewski ◽  
Leszek J. Chmielewski
Keyword(s):  
Logistic Regression ◽  
Decision Trees ◽  
Regression Models ◽  
Confidence Region ◽  
Texture Features ◽  
Stepwise Logistic Regression ◽  
Breast Lesions ◽  
Operating Characteristics ◽  
Rf Signals

Most of the methods of classification of breast lesions in ultrasound (US) images have been tested on B-mode images from the commercial equipment.  The new possibility of further analysis of this  problem showed up with the availability of a public database containing original raw  radio frequency (RF) signals. In particular, it appeared  that the original texture might contain  diagnostic information which could be modified in the typical image processing and which is more difficult  to perceive than the  details of  lesion shape/contour. In this paper a  detailed analysis of the lesion texture is conducted by means of the decision trees and  logistic regression. The decision trees turned out  useful mainly for selecting texture features to be used in the stepwise logistic regression. The RF signals database of 200 breast lesions  was used for testing the performance of the benign vs malignant lesion classifier. The Gray Level Cooccurrence Matrix (GLCM)  was calculated with the vertical/horizontal offset of up to five pixels. For each of these matrices six features were calculated resulting in a total of 210 features. Using these features a sufficient number of decision trees were generated to calculate pseudo-Receiver Operating Characteristics (ROCs). The outcome of this  process is a collection of generated trees for which the employed features are known. These features were then used for generating  generalized linear model by means of stepwise logistic regression. The analyzed regression  models included the coefficients of up-to-the second degree terms. The texture features were further completed by a single shape feature,  that is tumor circularity (TC). The automatic procedure for finding the exact mask of a lesion is also provided for the conditions when the acoustic shadowing makes it impossible to obtain the entire contour reliably and a half-contour has to be used. The selected logistic regression models gave  ROCs with the Area Under Curve (AUC) of up to 0.83 and the 95 \% confidence region (0.63 0.96). Analyzing classification results one comes to the conclusion that the tumor circularity, which is the most informative  among shape/contour features, is not essential against the background of textural features. The reported study shows that a relatively straightforward procedure can be employed  to obtain  benign vs malignant  classifier comparable with that originally used for the database of the raw RF signals and based on the more complicated segmentation of the parameter maps of homodyned K distribution.

scholarly journals Gene Selection in Cancer Classification Using Sparse Logistic Regression with L1/2 Regularization

2018 ◽  
Vol 8 (9) ◽  
pp. 1569 ◽  
Author(s):  
Shengbing Wu ◽  
Hongkun Jiang ◽  
Haiwei Shen ◽  
Ziyi Yang
Keyword(s):  
Logistic Regression ◽  
Gene Selection ◽  
Classification Performance ◽  
Cancer Classification ◽  
Sparse Logistic Regression ◽  
The Subject ◽  
Selection For ◽  
Microarray Datasets ◽  
Sparse Methods

In recent years, gene selection for cancer classification based on the expression of a small number of gene biomarkers has been the subject of much research in genetics and molecular biology. The successful identification of gene biomarkers will help in the classification of different types of cancer and improve the prediction accuracy. Recently, regularized logistic regression using the L 1 regularization has been successfully applied in high-dimensional cancer classification to tackle both the estimation of gene coefficients and the simultaneous performance of gene selection. However, the L 1 has a biased gene selection and dose not have the oracle property. To address these problems, we investigate L 1 / 2 regularized logistic regression for gene selection in cancer classification. Experimental results on three DNA microarray datasets demonstrate that our proposed method outperforms other commonly used sparse methods ( L 1 and L E N ) in terms of classification performance.

scholarly journals Classification of mislabelled microarrays using robust sparse logistic regression

2013 ◽  
Vol 29 (7) ◽  
pp. 870-877 ◽  
Author(s):  
Jakramate Bootkrajang ◽  
Ata Kabán
Keyword(s):  
Logistic Regression ◽  
Sparse Logistic Regression

scholarly journals Sparse logistic regression for whole-brain classification of fMRI data

NeuroImage ◽  
2010 ◽  
Vol 51 (2) ◽  
pp. 752-764 ◽  
Author(s):  
Srikanth Ryali ◽  
Kaustubh Supekar ◽  
Daniel A. Abrams ◽  
Vinod Menon
Keyword(s):  
Logistic Regression ◽  
Fmri Data ◽  
Whole Brain ◽  
Sparse Logistic Regression

scholarly journals An Additive Sparse Logistic Regularization Method for Cancer Classification in Microarray Data

2021 ◽  
Vol 18 (2) ◽  
Keyword(s):  
Logistic Regression ◽  
Microarray Data ◽  
High Dimensional ◽  
Cancer Data ◽  
Abnormal Growth ◽  
Sparse Logistic Regression ◽  
Genome Data ◽  
Regularization Techniques ◽  
Performance Of Algorithm

Now a day’s cancer has become a deathly disease due to the abnormal growth of the cell. Many researchers are working in this area for the early prediction of cancer. For the proper classification of cancer data, demands for the identification of proper set of genes by analyzing the genomic data. Most of the researchers used microarrays to identify the cancerous genomes. However, such kind of data is high dimensional where number of genes are more compared to samples. Also the data consists of many irrelevant features and noisy data. The classification technique deal with such kind of data influences the performance of algorithm. A popular classification algorithm (i.e., Logistic Regression) is considered in this work for gene classification. Regularization techniques like Lasso with L1 penalty, Ridge with L2 penalty, and hybrid Lasso with L1/2+2 penalty used to minimize irrelevant features and avoid overfitting. However, these methods are of sparse parametric and limits to linear data. Also methods have not produced promising performance when applied to high dimensional genome data. For solving these problems, this paper presents an Additive Sparse Logistic Regression with Additive Regularization (ASLR) method to discriminate linear and non-linear variables in gene classification. The results depicted that the proposed method proved to be the best-regularized method for classifying microarray data compared to standard methods

An Enhanced Technique for Classification of Fruits using Shape Color and Texture Features

2017 ◽  
Vol 7 (7) ◽  
pp. 408
Author(s):  
Yashpal Jitarwal ◽  
Tabrej Ahamad Khan ◽  
Pawan Mangal
Keyword(s):  
Image Processing ◽  
Texture Features ◽  
Automatic Classification ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Advance Technique ◽  
Time Required ◽  
Fruit Sorting ◽  
Human Inspection

In earlier times fruits were sorted manually and it was very time consuming and laborious task. Human sorted the fruits of the basis of shape, size and color. Time taken by human to sort the fruits is very large therefore to reduce the time and to increase the accuracy, an automatic classification of fruits comes into existence.To improve this human inspection and reduce time required for fruit sorting an advance technique is developed that accepts information about fruits from their images, and is called as Image Processing Technique.

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