Entropy-based feature extraction and decision tree induction for breast cancer diagnosis with standardized thermograph images

2010 ◽  
Vol 100 (3) ◽  
pp. 269-282 ◽  
Author(s):  
Ming-Yih Lee ◽  
Chi-Shih Yang
Keyword(s):  
Breast Cancer ◽  
Feature Extraction ◽  
Decision Tree ◽  
Cancer Diagnosis ◽  
Breast Cancer Diagnosis ◽  
Decision Tree Induction

scholarly journals Breast cancer diagnosis using feature extraction and boosted C5.0 decision tree algorithm with penalty factor

2022 ◽  
Vol 19 (3) ◽  
pp. 2193-2205
Author(s):  
Jian-xue Tian ◽  
◽  
Jue Zhang
Keyword(s):  
Breast Cancer ◽  
Feature Extraction ◽  
Decision Tree ◽  
Cancer Diagnosis ◽  
Class Imbalance ◽  
Breast Cancer Diagnosis ◽  
Decision Tree Algorithm ◽  
Tree Algorithm ◽  
Penalty Factor ◽  
C5.0 Decision Tree

<abstract><p>To overcome the two class imbalance problem among breast cancer diagnosis, a hybrid method by combining principal component analysis (PCA) and boosted C5.0 decision tree algorithm with penalty factor is proposed to address this issue. PCA is used to reduce the dimension of feature subset. The boosted C5.0 decision tree algorithm is utilized as an ensemble classifier for classification. Penalty factor is used to optimize the classification result. To demonstrate the efficiency of the proposed method, it is implemented on biased-representative breast cancer datasets from the University of California Irvine(UCI) machine learning repository. Given the experimental results and further analysis, our proposal is a promising method for breast cancer and can be used as an alternative method in class imbalance learning. Indeed, we observe that the feature extraction process has helped us improve diagnostic accuracy. We also demonstrate that the extracted features considering breast cancer issues are essential to high diagnostic accuracy.</p></abstract>

scholarly journals Resnet Based Feature Extraction with Decision Tree Classifier for Classificaton of Mammogram Images

2021 ◽  
Vol 12 (2) ◽  
pp. 1147-1153
Author(s):  
T. Sathya Priya, Et. al.
Keyword(s):  
Breast Cancer ◽  
Feature Extraction ◽  
Decision Tree ◽  
Breast Cancer Diagnosis ◽  
Classification Performance ◽  
Decision Tree Classifier ◽  
Tree Classifier ◽  
In The Beginning ◽  
Sensitivity Specificity ◽  
Mammogram Images

Right now, breast cancer is considered as a most important health problem among women over the world. The detection of breast cancer in the beginning stage can reduce the mortality rate to a considerable extent. Mammogram is an effective and regularly used technique for the detection and screening of breast cancer. The advanced deep learning (DL) techniques are utilized by radiologists for accurate finding and classification of medical images. This paper develops a new deep segmentation with residual network (DS-RN) based breast cancer diagnosis model using mammogram images. The presented DS-RN model involves preprocessing, Faster Region based Convolution Neural Network (R-CNN) (Faster R-CNN) with Inception v2 model based segmentation, feature extraction and classification. To classify the mammogram images, decision tree (DT) classifier model is used. A detailed simulation process is performed to ensure the betterment of the presented model on the Mini-MIAS dataset. The obtained experimental values stated that the DS-RN model has reached to a maximum classification performance with the maximum sensitivity, specificity, accuracy and F-Measure of 98.15%, 100%, 98.86% and 99.07% respectively.  

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