期刊名称：Journal of Automation, Mobile Robotics & Intelligent Systems (JAMRIS)
印刷版ISSN：1897-8649
电子版ISSN：2080-2145
出版年度：2020
卷号：14
期号：2
页码：74-80
DOI：10.14313/JAMRIS/2-2020/22
出版社：Industrial Research Inst. for Automation and Measurements, Warsaw
摘要：Mammography based breast cancer screening is very po‐ pular because of its lower costing and readily availability. For automated classification of mammogram images as benign or malignant machine learning techniques are in‐ volved. In this paper, a novel image descriptor which is based on the idea of Radon and Wavelet transform is proposed. This method is quite efficient as it performs well without any clinical information. Performance of the method is evaluated using six different classifiers na‐ mely: Bayesian network (BN), Linear discriminant analy‐ sis (LDA), Logistic, Support vector machine (SVM), Multi‐ layer perceptron (MLP) and Random Forest (RF) to choose the best performer. Considering the present experimen‐ tal framework, we found, in terms of area under the ROC curve (AUC), the proposed image descriptor outperforms, upto some extent, previous reported experiments using histogram based hand‐crafted methods, namely Histo‐ gram of Oriented Gradient (HOG) and Histogram of Gra‐ dient Divergence (HGD) and also Convolution Neural Net‐ work (CNN). Our experimental results show the highest AUC value of 0.986, when using only the carniocaudal (CC) view compared to when using only the mediolateral oblique (MLO) (0.738) or combining both views (0.838). These results thus proves the effectiveness of CC view over MLO for better mammogram mass classification.
