Breast Cancer Detection and Classification Using Support Vector Machines and Pulse Coupled Neural Network

2012 ◽  
pp. 269-279 ◽  
Author(s):  
Aboul Ella Hassanien ◽  
Nashwa El-Bendary ◽  
Miloš Kudělka ◽  
Václav Snášel
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Support Vector Machines ◽  
Cancer Detection ◽  
Breast Cancer Detection ◽  
Support Vector ◽  
Pulse Coupled Neural Network ◽  
Vector Machines

scholarly journals Machine Learning and Infrared Thermography for Breast Cancer Detection

Proceedings ◽  
2019 ◽  
Vol 27 (1) ◽  
pp. 45 ◽  
Author(s):  
Caroline Gonçalves ◽  
Amanda Leles ◽  
Lucimara Oliveira ◽  
Gilmar Guimaraes ◽  
Juliano Cunha ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Infrared Thermography ◽  
Cancer Detection ◽  
Low Cost ◽  
Breast Cancer Detection ◽  
Support Vector ◽  
Patients With Cancer ◽  
Vector Machines ◽  
Malignant Changes ◽  
Harmful Radiation

Breast cancer kills a large number of women around the world. Infrared thermography is a promising screening technique which does not involve harmful radiation for the patient and has a relatively low cost. This work proposes an approach for classifying patients into three different classes using infrared images: healthy patients, patients with benign changes and patients with cancer (malignant changes). A set of features is extracted from each image and two approaches are used in the classification process. The first is based on Artificial Neural Networks while the second is based on Support Vector Machines. The proposed approach shows a great potential to be used as a screening diagnosis technique for early breast cancer detection.

Classification of Benign and Malignant Breast Masses on Mammograms for Large Datasets using Core Vector Machines

2020 ◽  
Vol 16 (6) ◽  
pp. 703-710 ◽  
Author(s):  
Jebasonia Jebamony ◽  
Dheeba Jacob
Keyword(s):  
Breast Cancer ◽  
Machine Learning ◽  
Cancer Detection ◽  
Breast Cancer Detection ◽  
Misclassification Rate ◽  
Support Vector ◽  
Mass Detection ◽  
Training Process ◽  
Vector Machines

Background: Breast cancer is one of the most leading causes of cancer deaths among women. Early detection of cancer increases the survival rate of the affected women. Machine learning approaches that are used for classification of breast cancer usually takes a lot of processing time during the training process. This paper attempts to propose a Machine Learning approach for breast cancer detection in mammograms, which does not depend on the number of training samples. Objective: The paper aims to develop a core vector machine-based diagnosis system for breast cancer detection using the date from MIAS. The main motivation behind using this system is to reduce the computational and memory requirement for large training data and to improve the classification accuracy. Methods: The proposed method has four stages: 1) Pre-processing is done to extract the breast region using global thresholding and enhancement using histogram equalization; 2) identification of potential mass using Otsu thresholding; 3) feature extraction using Laws Texture energy measures; and 4) mass detection is done using Core vector machine (CVM) classifier. Results: Comparative analysis was done with different existing algorithms: Artificial Neural Network (ANN), Support Vector Machine (SVM), and Fuzzy Support Vector Machines (FSVM). The results illustrate that the proposed Core Vector Machine (CVM) classifier produced a promising result in terms of sensitivity (96.9%), misclassification rate (0.0443) and accuracy (95.89%). The time taken for training process is 0.0443, which is less when compared with other machine learning algorithms. Conclusion: Performance analysis shows that CVM classifier is superior to other classifiers like ANN, SVM and FSVM. The computational time of the CVM classifier during the training process was also analysed and found to be better than other discussed algorithms. The results achieved show that CVM classifier is the best algorithm for breast mass detection in mammograms.

A compact neural network for training support vector machines

2012 ◽  
Vol 86 ◽  
pp. 193-198 ◽  
Author(s):  
Yun Yang ◽  
Qiaochu He ◽  
Xiaolin Hu
Keyword(s):  
Neural Network ◽  
Support Vector Machines ◽  
Support Vector ◽  
Training Support ◽  
Vector Machines

scholarly journals Segmentation of HER2 protein overexpression in immunohistochemically stained breast cancer images using Support Vector Machines

2016 ◽  
Vol 762 ◽  
pp. 012050 ◽  
Author(s):  
Raquel Pezoa ◽  
Luis Salinas ◽  
Claudio Torres ◽  
Steffen Härtel ◽  
Cristián Maureira-Fredes ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Support Vector Machines ◽  
Support Vector ◽  
Protein Overexpression ◽  
Her2 Protein ◽  
Vector Machines

Thermography Based Breast Cancer Detection Using Texture Features and Support Vector Machine

2010 ◽  
Vol 36 (3) ◽  
pp. 1503-1510 ◽  
Author(s):  
U. Rajendra Acharya ◽  
E. Y. K. Ng ◽  
Jen-Hong Tan ◽  
S. Vinitha Sree
Keyword(s):  
Breast Cancer ◽  
Support Vector Machine ◽  
Cancer Detection ◽  
Texture Features ◽  
Breast Cancer Detection ◽  
Support Vector
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