Radon‐wavelet based novel image descriptor for mammogram mass classification

• Autorzy: Obaidullah Sk Md , Ahmed Sajib , Gonçalves Teresa , Rato Luís

Identyfikatory

DOI

10.14313/JAMRIS/2‐2020/22

• Języki publikacji: EN

Abstrakty

EN

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.

Słowa kluczowe

EN

image descriptor; radon transform; mammography; breast cancer; classification

• Wydawca: Łukasiewicz Industrial Research Institute for Automation and Measurements PIAP
• Czasopismo: Journal of Automation Mobile Robotics and Intelligent Systems
• Rocznik: 2020
• Tom: Vol. 14, No. 2
• Strony: 74--80
• Opis fizyczny: Bibliogr. 27 poz.

Twórcy

autor

Obaidullah Sk Md

• Department of Informatics, University of Èvora, Rua Romão Ramalho, 59, 7000 671 Èvora, Portugal

autor

Ahmed Sajib

• Department of Informatics, University of Èvora, Rua Romão Ramalho, 59, 7000 671 Èvora, Portugal

autor

Gonçalves Teresa

• Department of Informatics, University of Èvora, Rua Romão Ramalho, 59, 7000 671 Èvora, Portugal

autor

Rato Luís

• Department of Informatics, University of Èvora, Rua Romão Ramalho, 59, 7000 671 Èvora, Portugal

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Uwagi

Opracowanie rekordu ze środków MNiSW, umowa Nr 461252 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2020).