Breast thermal images classification using optimal feature selectors and classifiers

The Journal of Engineering

Fatemeh Pak¹  Mohammad Firouzmand²  AmirEhsan Lashkari² 

[1] Information Technology, Iranian Research Organization for Science and Technology (IROST), Tehran, Iran;Department of Bio-Medical Engineering, Institute of Electrical Engineering &

关键词: frequency domain;    AdaBoost;    ice testing;    supervised algorithms;    sympathetic nervous system;    cold stimulus;    breast thermal image classification;    TH labelling procedures;    k-nearest neighbours;    full automatic technique;    genetic algorithm;    Naive Bayes;    frequency domain features;    native database;    sequential floating forward selection;    supervised learning techniques;    probability neural network;    full automatic operation;    histogram features;    cancerous mammary tissue;    statistical features;    sequential floating backward selection;    breast cancer detection;    region of interest;    support vector machine;    morphological features;    grey-level cooccurrence matrix features;   

DOI  :  10.1049/joe.2016.0060

学科分类：工程和技术（综合）

来源: IET

【 摘 要 】

In this study, a full automatic technique has been presented to assist physicians in early detection of breast cancer based on different degrees. First the region of interest is determined using full automatic operation and the quality of image is improved. Then, some features including statistical, morphological, frequency domain, histogram and grey-level co-occurrence matrix features are extracted from segmented right and left breasts. Subsequently, to achieve the best features and increase the accuracy of the proposed method, feature selectors such as minimum redundancy and maximum relevance, sequential forward selection, sequential backward selection, sequential floating forward selection, sequential floating backward selection and genetic algorithm have been used. Finally, to classify and TH labeling procedures, supervised learning techniques such as AdaBoost, support vector machine, nearest neighbor, Naïve Bayes and probability neural network are applied and compared with each other. The results obtained on native database showed the significant performance of the proposed algorithm in comprising to the similar studies. The experimental results gave the best mean accuracy of 88.03% for only using 0° image with combination of mRMR and AdaBoost and for combination of 3 degrees with combination of GA and AdaBoost.

【 授权许可】

CC BY