Computer aided detection of mammographic mass using exact Gaussian–Hermite moments

被引：14

作者：

Eltoukhy M.M. ^{[1
]}

Elhoseny M. ^{[2
]}

Hosny K.M. ^{[3
]}

Singh A.K. ^{[4
]}

机构：

[1] Computer Science Department, Faculty of Computers and Informatics, Suez Canal University, Ismailia

[2] Faculty of Computers and Information, Mansoura University, El Mansûra

[3] Department of Information Technology, Faculty of Computers and Informatics, Zagazig University, Zagazig

[4] Jaypee University of Information Technology (JUIT), Waknaghat, Himachal Pradesh, Solan

来源：

Journal of Ambient Intelligence and Humanized Computing | 2024年 / 15卷 / 01期

关键词：

Breast cancer; Computer aided diagnose; Gaussian–Hermite; Health informatics;

D O I：

10.1007/s12652-018-0905-1

中图分类号：

学科分类号：

摘要：

Breast cancer is one of the common cancer deaths in women worldwide. Early detection is the key to reduce the mortality rate. Clinical trials have shown that computer aided systems (CAD) have improved the accuracy of breast cancer detection. This paper proposed a highly accurate CAD system based on extracting highly significant features using exact Gaussian–Hermite moments. The obtained feature vector is presented to K-NN, random forests and AdaBoost classifiers. The proposed system is evaluated using two different datasets namely IRMA and MIAS. The evaluation metrics of accuracy, TP, FP and area under ROC curve using 10-fold cross-validation are calculated. The results indicate the usefulness of the proposed exact Gaussian–Hermite moments features for distinguishing between normal and abnormal lesions and the superiority of the moments features compared with the conventional methods. © Springer-Verlag GmbH Germany, part of Springer Nature 2018.

引用

页码：1139 / 1147

页数：8

共 39 条

[1]

Abdelaziz A., Elhoseny M., Salama A.S., Riad A.M., A machine learning model for improving healthcare services on cloud computing environment, Measurement, 119, pp. 117-128, (2018)

[2]

Abdelwahed N.M., Eltoukhy M.M., Wahed M.E., Computer aided system for breast cancer diagnosis in ultrasound images, J Ecol Health Environ, 3, 3, pp. 71-76, (2015)

[3]

American Cancer Society, Cancer facts & figures 2017, (2017)

[4]

Beura S., Majhi B., Dash R., Mammogram classification using two-dimensional discrete wavelet transform and gray-level co-occurrence matrix for detection of breast cancer, Neurocomputing, 154, pp. 1-14, (2015)

[5]

Bruno D.O.T., Nascimento D., Ramos M.Z., Batista R.P., Neves V.R., Martins A.S., LBP operators on curvelet coefficients as an algorithm to describe texture in breast cancer tissues, Expert Syst Appl, 55, pp. 329-340, (2016)

[6]

Chakraborty J., Midya A., Rabidas R., Computer-aided detection and diagnosis of mammographic masses using multi-resolution analysis of oriented tissue patterns, Expert Syst Appl, 99, pp. 168-179, (2018)

[7]

Cheng H.D., Cai X., Chen X., Hu L., Lou X., Computer-aided detection and classification of microcalcifications in mammograms: a survey, Pattern Recognit, 36, 12, pp. 2967-2991, (2003)

[8]

Cvetkovic J., Nenadovic M., Depression in breast cancer patients, Psychiatry Res, 240, pp. 343-347, (2016)

[9]

Darwish A., Hassanien A.E., Elhoseny M., Sangaiah A.K., Muhammad K., The impact of the hybrid platform of internet of things and cloud computing on healthcare systems: opportunities, challenges, and open problems, J Ambient Intell Humaniz Comput, (2017)

[10]

Dhahbi S., Barhoumi W., Zagrouba E., Breast cancer diagnosis in digitized mammograms using curvelet moments, Comput Biol Med, 64, pp. 79-90, (2015)

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