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Authors: Obaidullah, Sk Md
Ahmed, Sajib
Gonçalves, Teresa
Rato, Luis
Keywords: Image descriptor
radon transform
breast cancer
Issue Date: Jun-2020
Publisher: Łukasiewicz PIAP
Citation: Md Obaidullah, S., Ahmed, S., Gonçalves, T., & Rato, L. (2019). Radon-Wavelet Based Novel Image Descriptor for Mammogram Mass Classification. Journal of Automation, Mobile Robotics and Intelligent Systems, 14(2), 74–80.
Abstract: Mammography based breast cancer screening is very popular because of its lower costing and readily availability. For automated classification of mammogram images as benign or malignant machine learning techniques are involved. 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 namely: Bayesian network (BN), Linear discriminant analysis (LDA), Logistic, Support vector machine (SVM), Multilayer perceptron (MLP) and Random Forest (RF) to choose the best performer. Considering the present experimental 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 Histogram of Oriented Gradient (HOG) and Histogram of Gradient Divergence (HGD) and also Convolution Neural Network (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.
Type: article
Appears in Collections:INF - Publicações - Artigos em Revistas Internacionais Com Arbitragem Científica

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