A statistical feature selection method for lung cancer classification in CT scans

2013 ◽  
Author(s):  
Hamada R. H. Al-Absi ◽  
Brahim Belhaouari Samir
Keyword(s):  
Lung Cancer ◽  
Feature Selection ◽  
Feature Selection Method ◽  
Selection Method ◽  
Cancer Classification ◽  
Ct Scans ◽  
Statistical Feature

scholarly journals An Enhancement in Cancer Classification Accuracy Using a Two-Step Feature Selection Method Based on Artificial Neural Networks with 15 Neurons

Symmetry ◽  
2020 ◽  
Vol 12 (2) ◽  
pp. 271 ◽  
Author(s):  
Md Akizur Rahman ◽  
Ravie Chandren Muniyandi
Keyword(s):  
Neural Network ◽  
Feature Selection ◽  
Classification Accuracy ◽  
Feature Selection Method ◽  
Selection Method ◽  
Cancer Classification ◽  
Neuron Network ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Risk Of Cancer

An artificial neural network (ANN) is a tool that can be utilized to recognize cancer effectively. Nowadays, the risk of cancer is increasing dramatically all over the world. Detecting cancer is very difficult due to a lack of data. Proper data are essential for detecting cancer accurately. Cancer classification has been carried out by many researchers, but there is still a need to improve classification accuracy. For this purpose, in this research, a two-step feature selection (FS) technique with a 15-neuron neural network (NN), which classifies cancer with high accuracy, is proposed. The FS method is utilized to reduce feature attributes, and the 15-neuron network is utilized to classify the cancer. This research utilized the benchmark Wisconsin Diagnostic Breast Cancer (WDBC) dataset to compare the proposed method with other existing techniques, showing a significant improvement of up to 99.4% in classification accuracy. The results produced in this research are more promising and significant than those in existing papers.

Flow Feature Selection Method Based on Statistics

2014 ◽  
Vol 1030-1032 ◽  
pp. 1709-1712
Author(s):  
Kai Min Song ◽  
Xun Yi Ren
Keyword(s):  
Feature Selection ◽  
Feature Selection Method ◽  
Selection Method ◽  
Experimental Results ◽  
Identification Algorithm ◽  
Selection Algorithm ◽  
Feature Selection Algorithm ◽  
Statistical Feature ◽  
Flow Feature

Through the research on the flow identification algorithm based on statistical feature, this paper puts forward the statistical feature selection algorithm in order to reduce the number of features in identification, increase the speed of the flow identification, the experimental results show that the algorithm can effectively reduce the amount of features, improve the efficiency of identification.

scholarly journals An Improved Feature Selection Based on Effective Range for Classification

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Jianzhong Wang ◽  
Shuang Zhou ◽  
Yugen Yi ◽  
Jun Kong
Keyword(s):  
Feature Selection ◽  
Gene Selection ◽  
State Of The Art ◽  
Feature Selection Method ◽  
Selection Method ◽  
Effective Range ◽  
Inclusion Relation ◽  
Statistical Feature ◽  
Selection Approach ◽  
Feature Selection Approach

Feature selection is a key issue in the domain of machine learning and related fields. The results of feature selection can directly affect the classifier’s classification accuracy and generalization performance. Recently, a statistical feature selection method named effective range based gene selection (ERGS) is proposed. However, ERGS only considers the overlapping area (OA) among effective ranges of each class for every feature; it fails to handle the problem of the inclusion relation of effective ranges. In order to overcome this limitation, a novel efficient statistical feature selection approach called improved feature selection based on effective range (IFSER) is proposed in this paper. In IFSER, an including area (IA) is introduced to characterize the inclusion relation of effective ranges. Moreover, the samples’ proportion for each feature of every class in both OA and IA is also taken into consideration. Therefore, IFSER outperforms the original ERGS and some other state-of-the-art algorithms. Experiments on several well-known databases are performed to demonstrate the effectiveness of the proposed method.

scholarly journals Predicting Lung Cancer Patients’ Survival Time via Logistic Regression-based Models in a Quantitative Radiomic Framework

2019 ◽  
Author(s):  
S P Shayesteh ◽  
I Shiri ◽  
A H Karami ◽  
R Hashemian ◽  
S Kooranifar ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Logistic Regression ◽  
Feature Selection ◽  
Mutual Information ◽  
Cancer Patients ◽  
Feature Selection Method ◽  
Selection Method ◽  
Classification Models ◽  
Lung Cancer Patients ◽  
Pre Treatment

Objectives: The aim of this study was to predict the survival time of lung cancer patients using the advantages of both radiomics and logistic regression-based classification models.Material and Methods: Fifty-nine patients with primary lung adenocarcinoma were included in this retrospective study and pre-treatment contrast-enhanced CT images were acquired. The patients lived more than 2 years were classified as the ‘Alive’ class and otherwise as the ‘Dead’ class. In our proposed quantitative radiomic framework, we first extracted the associated regions of each lung lesion from pre-treatment CT images for each patient via grow cut segmentation algorithm. Then, 40 radiomic features were extracted from the segmented lung lesions. In order to enhance the generalizability of the classification models, the mutual information-based feature selection method was applied to each feature vector. We investigated the performance of six logistic regression-based classification models with consider to acceptable evaluation measures such as F1 score and accuracy.Results: It was observed that the mutual information feature selection method can help the classifier to achieve better predictive results. In our study, the Logistic regression (LR) and Dual Coordinate Descent method for Logistic Regression (DCD-LR) models achieved the best results indicating that these classification models have strong potential for classifying the more important class (i.e., the ‘Alive’ class).Conclusion: The proposed quantitative radiomic framework yielded promising results, which can guide physicians to make better and more precise decisions and increase the chance of treatment success.

Classification of lung cancer using ensemble-based feature selection and machine learning methods

2015 ◽  
Vol 11 (3) ◽  
pp. 791-800 ◽  
Author(s):  
Zhihua Cai ◽  
Dong Xu ◽  
Qing Zhang ◽  
Jiexia Zhang ◽  
Sai-Ming Ngai ◽  
...  
Keyword(s):  
Machine Learning ◽  
Lung Cancer ◽  
Feature Selection ◽  
Feature Selection Method ◽  
Selection Method ◽  
Learning Methods ◽  
Machine Learning Methods

The ensemble-based feature selection method presents the merit of acquisition of more informative and compact features than those obtained by individual methods.

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