scholarly journals Correction to: Computer-aided diagnosis applied to MRI images of brain tumor using cognition based modified level set and optimized ANN classifier

2018 ◽  
Vol 79 (5-6) ◽  
pp. 3601-3601 ◽  
Author(s):  
Virupakshappa ◽  
Basavaraj Amarapur
Keyword(s):  
Brain Tumor ◽  
Level Set ◽  
Computer Aided Diagnosis ◽  
Computer Aided ◽  
Ann Classifier ◽  
Aided Diagnosis

scholarly journals Mammogram Classification using Curvelet Coefficients and Gray Level Co-Occurrence Matrix for Detection of Breast Cancer

2019 ◽  
Vol 8 (12) ◽  
pp. 4819-4824
Keyword(s):  
Breast Cancer ◽  
Curvelet Transform ◽  
Test Methods ◽  
Computer Aided Diagnosis ◽  
Computer Aided ◽  
Energy Feature ◽  
Artificial Neural Network Ann ◽  
Ann Classifier ◽  
Occurrence Matrix ◽  
Aided Diagnosis

Computer-aided diagnosis system plays an important role in diagnosis and detection of breast cancer. In computer-aided diagnosis, feature extraction is one of the important steps. In this paper, we have proposed a method based on curvelet transform to classify mammogram images as normal -abnormal, benign and malignant. The feature vector is computed from the approximation coefficients. Directional energy is also calculated for all sub-bands. To select the efficient feature we used t-test and f-test methods. The selected feature is applied to Artificial Neural Network (ANN) classifier for classification. The effectiveness of the proposed method has been tested on MIAS database. The performance measures are computed with respect to normal vs. abnormal and benign vs. malignant for using approximation subband and energy feature of all curvelet coefficients. The highest classification accuracy of 95.34% is achieved for normal vs. abnormal and 80.86% is achieved for benign vs malignant class using energy feature of all curvelet coefficients.

Computer-aided diagnosis of human brain tumor through MRI: A survey and a new algorithm

2014 ◽  
Vol 41 (11) ◽  
pp. 5526-5545 ◽  
Author(s):  
El-Sayed A. El-Dahshan ◽  
Heba M. Mohsen ◽  
Kenneth Revett ◽  
Abdel-Badeeh M. Salem
Keyword(s):  
Brain Tumor ◽  
Human Brain ◽  
Computer Aided Diagnosis ◽  
Human Brain Tumor ◽  
Computer Aided ◽  
Aided Diagnosis

scholarly journals A Computer-Aided Diagnosis System for Dynamic Contrast-Enhanced MR Images Based on Level Set Segmentation and ReliefF Feature Selection

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Zhiyong Pang ◽  
Dongmei Zhu ◽  
Dihu Chen ◽  
Li Li ◽  
Yuanzhi Shao
Keyword(s):  
Feature Selection ◽  
Level Set ◽  
Computer Aided Diagnosis ◽  
Support Vector ◽  
Svm Classifier ◽  
Segmentation Method ◽  
Cad System ◽  
Computer Aided ◽  
Resampling Method ◽  
Aided Diagnosis

This study established a fully automated computer-aided diagnosis (CAD) system for the classification of malignant and benign masses via breast magnetic resonance imaging (BMRI). A breast segmentation method consisting of a preprocessing step to identify the air-breast interfacing boundary and curve fitting for chest wall line (CWL) segmentation was included in the proposed CAD system. The Chan-Vese (CV) model level set (LS) segmentation method was adopted to segment breast mass and demonstrated sufficiently good segmentation performance. The support vector machine (SVM) classifier with ReliefF feature selection was used to merge the extracted morphological and texture features into a classification score. The accuracy, sensitivity, and specificity measurements for the leave-half-case-out resampling method were 92.3%, 98.2%, and 76.2%, respectively. For the leave-one-case-out resampling method, the measurements were 90.0%, 98.7%, and 73.8%, respectively.

Sign in / Sign up

Export Citation Format

Share Document