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Erschienen in: Journal of Medical Systems 4/2016

01.04.2016 | Systems-Level Quality Improvement

An Improved CAD System for Breast Cancer Diagnosis Based on Generalized Pseudo-Zernike Moment and Ada-DEWNN Classifier

verfasst von: Satya P. Singh, Shabana Urooj

Erschienen in: Journal of Medical Systems | Ausgabe 4/2016

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Abstract

In this paper, a novel framework of computer-aided diagnosis (CAD) system has been presented for the classification of benign/malignant breast tissues. The properties of the generalized pseudo-Zernike moments (GPZM) and pseudo-Zernike moments (PZM) are utilized as suitable texture descriptors of the suspicious region in the mammogram. An improved classifier- adaptive differential evolution wavelet neural network (Ada-DEWNN) is proposed to improve the classification accuracy of the CAD system. The efficiency of the proposed system is tested on mammograms from the Mammographic Image Analysis Society (mini-MIAS) database using the leave-one-out cross validation as well as on mammograms from the Digital Database for Screening Mammography (DDSM) database using 10-fold cross validation. The proposed method on MIAS-database attains a fair accuracy of 0.8938 and AUC of 0.935 (95 % CI = 0.8213–0.9831). The proposed method is also tested for in-plane rotation and found to be highly rotation invariant. In addition, the proposed classifier is tested and compared with some well-known existing methods using receiver operating characteristic (ROC) analysis using DDSM- database. It is concluded the proposed classifier has better area under the curve (AUC) (0.9289) and highly précised with 95 % CI, 0.8216 to 0.9834 and 0.0384 standard error.
Literatur
1.
Zurück zum Zitat Lee, H., and Chen, Y. P. P., Image based computer aided diagnosis system for cancer detection. Expert Syst. Appl. 42(12):5356–5365, 2015.CrossRef Lee, H., and Chen, Y. P. P., Image based computer aided diagnosis system for cancer detection. Expert Syst. Appl. 42(12):5356–5365, 2015.CrossRef
2.
Zurück zum Zitat Rouhi, R., Jafari, M., Kasaei, S., and Keshavarzian, P., Benign and malignant breast tumors classification based on region growing and CNN segmentation. Expert Syst. Appl. 42(3):990–1002, 2015.CrossRef Rouhi, R., Jafari, M., Kasaei, S., and Keshavarzian, P., Benign and malignant breast tumors classification based on region growing and CNN segmentation. Expert Syst. Appl. 42(3):990–1002, 2015.CrossRef
3.
Zurück zum Zitat Sharaf-El-Deen, D. A., Moawad, I. F., and Khalifa, M. E., A new hybrid case-based reasoning approach for medical diagnosis systems. J. Med. Syst. 38(2):1–11, 2014.CrossRef Sharaf-El-Deen, D. A., Moawad, I. F., and Khalifa, M. E., A new hybrid case-based reasoning approach for medical diagnosis systems. J. Med. Syst. 38(2):1–11, 2014.CrossRef
4.
Zurück zum Zitat Srivastava, S., Sharma, N., Singh, S. K., and Srivastava, R., Quantitative analysis of a general framework of a CAD tool for breast cancer detection from mammograms. J. Med. Imaging Health Inform 4(5):654–674, 2014.CrossRef Srivastava, S., Sharma, N., Singh, S. K., and Srivastava, R., Quantitative analysis of a general framework of a CAD tool for breast cancer detection from mammograms. J. Med. Imaging Health Inform 4(5):654–674, 2014.CrossRef
5.
Zurück zum Zitat Chan, H. P., Doi, K., Vyborny, C. J., Schmidt, R. A., Metz, C. E., Lam, K. L., Ogura, T., Wu, Y., and MacMahon, H., Improvement in radiologists’ detection of clustered microcalcifications on mammograms: the potential of computer-aided diagnosis. Investig. Radiol. 25:1102–1110, 1990.CrossRef Chan, H. P., Doi, K., Vyborny, C. J., Schmidt, R. A., Metz, C. E., Lam, K. L., Ogura, T., Wu, Y., and MacMahon, H., Improvement in radiologists’ detection of clustered microcalcifications on mammograms: the potential of computer-aided diagnosis. Investig. Radiol. 25:1102–1110, 1990.CrossRef
6.
Zurück zum Zitat Samala, R. K., Chan, H. P., Lu, Y., Hadjiiski, L., Wei, J., Sahiner, B., and Helvie, M. A., Computer-aided detection of clustered microcalcifications in multiscale bilateral filtering regularized reconstructed digital breast tomosynthesis volume. Med. Phys. 41(2):021901, 2014.CrossRefPubMedPubMedCentral Samala, R. K., Chan, H. P., Lu, Y., Hadjiiski, L., Wei, J., Sahiner, B., and Helvie, M. A., Computer-aided detection of clustered microcalcifications in multiscale bilateral filtering regularized reconstructed digital breast tomosynthesis volume. Med. Phys. 41(2):021901, 2014.CrossRefPubMedPubMedCentral
7.
Zurück zum Zitat Moon, W. K., Lo, C. M., Goo, J. M., Bae, M. S., Chang, J. M., Huang, C. S., and Chang, R. F., Quantitative analysis for breast density estimation in low dose chest CT scans. J. Med. Syst. 38(3):1–9, 2014.CrossRef Moon, W. K., Lo, C. M., Goo, J. M., Bae, M. S., Chang, J. M., Huang, C. S., and Chang, R. F., Quantitative analysis for breast density estimation in low dose chest CT scans. J. Med. Syst. 38(3):1–9, 2014.CrossRef
8.
Zurück zum Zitat Sampat, M. P., Bovik, A. C., Whitman, G. J., and Markey, M. K., A model-based framework for the detection of spiculated masses on mammographya. Med. Phys. 35(5):2110–2123, 2008.CrossRefPubMed Sampat, M. P., Bovik, A. C., Whitman, G. J., and Markey, M. K., A model-based framework for the detection of spiculated masses on mammographya. Med. Phys. 35(5):2110–2123, 2008.CrossRefPubMed
9.
Zurück zum Zitat Ramos-Pollán, R., Guevara-López, M. A., Suárez-Ortega, C., Díaz-Herrero, G., Franco-Valiente, J. M., Rubio-del-Solar, M., González-de-Posada, N., Vaz, M. A. P., Loureiro, J., and Ramos, I., Discovering mammography-based machine learning classifiers for breast cancer diagnosis. J. Med. Syst. 36(4):2259–2269, 2012. Ramos-Pollán, R., Guevara-López, M. A., Suárez-Ortega, C., Díaz-Herrero, G., Franco-Valiente, J. M., Rubio-del-Solar, M., González-de-Posada, N., Vaz, M. A. P., Loureiro, J., and Ramos, I., Discovering mammography-based machine learning classifiers for breast cancer diagnosis. J. Med. Syst. 36(4):2259–2269, 2012.
10.
Zurück zum Zitat Elter, M., and Horsch, A., CADx of mammographic masses and clustered microcalcifications: a review. Med. Phys. 36(6):2052–2068, 2009.CrossRefPubMed Elter, M., and Horsch, A., CADx of mammographic masses and clustered microcalcifications: a review. Med. Phys. 36(6):2052–2068, 2009.CrossRefPubMed
11.
Zurück zum Zitat Dutt, V., and Greenleaf, J. F., Adaptive speckle reduction filter for log-compressed B-scan images. IEEE Trans. Med. Imaging 15(6):802–813, 1996.CrossRefPubMed Dutt, V., and Greenleaf, J. F., Adaptive speckle reduction filter for log-compressed B-scan images. IEEE Trans. Med. Imaging 15(6):802–813, 1996.CrossRefPubMed
12.
Zurück zum Zitat Compas, C. B., Wong, E. Y., Huang, X., Sampath, S., Lin, B., Pal, P., Papademetris, X., Thiele, K., Dione, D. P., Stacy, M., Staib, L. H., Sinusas, A. J., O’Donnell, M., and Duncan, J. S., Correction to “Radial Basis Functions for Combining Shape and Speckle Tracking in 4D Echocardiography” [Jun 14 1275–1289]. IEEE Trans. Med. Imaging 34(2):690–690, 2015. Compas, C. B., Wong, E. Y., Huang, X., Sampath, S., Lin, B., Pal, P., Papademetris, X., Thiele, K., Dione, D. P., Stacy, M., Staib, L. H., Sinusas, A. J., O’Donnell, M., and Duncan, J. S., Correction to “Radial Basis Functions for Combining Shape and Speckle Tracking in 4D Echocardiography” [Jun 14 1275–1289]. IEEE Trans. Med. Imaging 34(2):690–690, 2015.
13.
Zurück zum Zitat Achim, A., Bezerianos, A., and Tsakalides, P., Novel Bayesian multiscale method for speckle removal in medical ultrasound images. IEEE Trans. Med. Imaging 20(8):772–783, 2001.CrossRefPubMed Achim, A., Bezerianos, A., and Tsakalides, P., Novel Bayesian multiscale method for speckle removal in medical ultrasound images. IEEE Trans. Med. Imaging 20(8):772–783, 2001.CrossRefPubMed
14.
Zurück zum Zitat Jose, S., and Chandy, D. A., Content based mammogram retrieval using biorthogonal wavelet filters in DDSM database. In: 2014 International Conference on Green Computing Communication and Electrical Engineering (ICGCCEE). pp. 1–6. IEEE, 2014. Jose, S., and Chandy, D. A., Content based mammogram retrieval using biorthogonal wavelet filters in DDSM database. In: 2014 International Conference on Green Computing Communication and Electrical Engineering (ICGCCEE). pp. 1–6. IEEE, 2014.
15.
Zurück zum Zitat Tsantis, S., Spiliopoulos, S., Skouroliakou, A., Karnabatidis, D., Hazle, J. D., and Kagadis, G. C., Multiresolution edge detection using enhanced fuzzy c-means clustering for ultrasound image speckle reduction. Med. Phys. 41(7):072903, 2014.CrossRefPubMed Tsantis, S., Spiliopoulos, S., Skouroliakou, A., Karnabatidis, D., Hazle, J. D., and Kagadis, G. C., Multiresolution edge detection using enhanced fuzzy c-means clustering for ultrasound image speckle reduction. Med. Phys. 41(7):072903, 2014.CrossRefPubMed
16.
Zurück zum Zitat Stetson, P. F., Sommer, F. G., and Macovski, A., Lesion contrast enhancement in medical ultrasound imaging. IEEE Trans. Med. Imaging 16(4):416–425, 1997.CrossRefPubMed Stetson, P. F., Sommer, F. G., and Macovski, A., Lesion contrast enhancement in medical ultrasound imaging. IEEE Trans. Med. Imaging 16(4):416–425, 1997.CrossRefPubMed
17.
Zurück zum Zitat Abbas, A. A., Guo, X., Tan, W. H., and Jalab, H. A., Combined spline and B-spline for an improved automatic skin lesion segmentation in dermoscopic images using optimal color channel. J. Med. Syst. 38(8):1–8, 2014.CrossRef Abbas, A. A., Guo, X., Tan, W. H., and Jalab, H. A., Combined spline and B-spline for an improved automatic skin lesion segmentation in dermoscopic images using optimal color channel. J. Med. Syst. 38(8):1–8, 2014.CrossRef
18.
Zurück zum Zitat Horsch, K., Giger, M. L., Venta, L. A., and Vyborny, C. J., Computerized diagnosis of breast lesions on ultrasound. Med. Phys. 29(2):157–164, 2002.CrossRefPubMed Horsch, K., Giger, M. L., Venta, L. A., and Vyborny, C. J., Computerized diagnosis of breast lesions on ultrasound. Med. Phys. 29(2):157–164, 2002.CrossRefPubMed
19.
Zurück zum Zitat Moon, W. K., Huang, Y. S., Lo, C. M., Huang, C. S., Bae, M. S., Kim, W. H., and Chang, R. F., Computer-aided diagnosis for distinguishing between triple-negative breast cancer and fibroadenomas based on ultrasound texture features. Med. Phys. 42(6):3024–3035, 2015.CrossRefPubMed Moon, W. K., Huang, Y. S., Lo, C. M., Huang, C. S., Bae, M. S., Kim, W. H., and Chang, R. F., Computer-aided diagnosis for distinguishing between triple-negative breast cancer and fibroadenomas based on ultrasound texture features. Med. Phys. 42(6):3024–3035, 2015.CrossRefPubMed
20.
Zurück zum Zitat Madabhushi, A., and Metaxas, D. N., Combining low-, high-level and empirical domain knowledge for automated segmentation of ultrasonic breast lesions. IEEE Trans. Med. Imaging 22(2):155–169, 2003.CrossRefPubMed Madabhushi, A., and Metaxas, D. N., Combining low-, high-level and empirical domain knowledge for automated segmentation of ultrasonic breast lesions. IEEE Trans. Med. Imaging 22(2):155–169, 2003.CrossRefPubMed
21.
Zurück zum Zitat Noble, J. A., and Boukerroui, D., Ultrasound image segmentation: a survey. IEEE Trans. Med. Imaging 25(8):987–1010, 2006.CrossRefPubMed Noble, J. A., and Boukerroui, D., Ultrasound image segmentation: a survey. IEEE Trans. Med. Imaging 25(8):987–1010, 2006.CrossRefPubMed
22.
Zurück zum Zitat Torbati, N., Ayatollahi, A., and Kermani, A., An efficient neural network based method for medical image segmentation. Comput. Biol. Med. 44:76–87, 2014.CrossRefPubMed Torbati, N., Ayatollahi, A., and Kermani, A., An efficient neural network based method for medical image segmentation. Comput. Biol. Med. 44:76–87, 2014.CrossRefPubMed
23.
Zurück zum Zitat Tahmasbi, A., Saki, F., and Shokouhi, S. B., Classification of benign and malignant masses based on Zernike moments. Comput. Biol. Med. 41(8):726–735, 2011.CrossRefPubMed Tahmasbi, A., Saki, F., and Shokouhi, S. B., Classification of benign and malignant masses based on Zernike moments. Comput. Biol. Med. 41(8):726–735, 2011.CrossRefPubMed
24.
Zurück zum Zitat Saki, F., Tahmasbi, A., Soltanian-Zadeh, H., and Shokouhi, S. B., Fast opposite weight learning rules with application in breast cancer diagnosis. Comput. Biol. Med. 43(1):32–41, 2013.CrossRefPubMed Saki, F., Tahmasbi, A., Soltanian-Zadeh, H., and Shokouhi, S. B., Fast opposite weight learning rules with application in breast cancer diagnosis. Comput. Biol. Med. 43(1):32–41, 2013.CrossRefPubMed
25.
Zurück zum Zitat Liu, X., and Tang, J., Mass classification in mammograms using selected geometry and texture features, and a new SVM-based feature selection method. IEEE Syst. J. 8(3):910–920, 2014.CrossRef Liu, X., and Tang, J., Mass classification in mammograms using selected geometry and texture features, and a new SVM-based feature selection method. IEEE Syst. J. 8(3):910–920, 2014.CrossRef
26.
Zurück zum Zitat Reyad, Y. A., Berbar, M. A., and Hussain, M., Comparison of statistical, LBP, and multi-resolution analysis features for breast mass classification. J. Med. Syst. 38(9):1–15, 2014.CrossRef Reyad, Y. A., Berbar, M. A., and Hussain, M., Comparison of statistical, LBP, and multi-resolution analysis features for breast mass classification. J. Med. Syst. 38(9):1–15, 2014.CrossRef
27.
Zurück zum Zitat Zhang, X., Liu, W., Dundar, M., Badve, S., and Zhang, S., Towards large-scale histopathological image analysis: hashing-based image retrieval. IEEE Trans. Med. Imaging 34(2):496–506, 2015.CrossRefPubMed Zhang, X., Liu, W., Dundar, M., Badve, S., and Zhang, S., Towards large-scale histopathological image analysis: hashing-based image retrieval. IEEE Trans. Med. Imaging 34(2):496–506, 2015.CrossRefPubMed
28.
Zurück zum Zitat Casti, P., Mencattini, A., Salmeri, M., and Rangayyan, R. M., Analysis of structural similarity in mammograms for detection of bilateral asymmetry. IEEE Trans. Med. Imaging 34(2):662–671, 2015.CrossRefPubMed Casti, P., Mencattini, A., Salmeri, M., and Rangayyan, R. M., Analysis of structural similarity in mammograms for detection of bilateral asymmetry. IEEE Trans. Med. Imaging 34(2):662–671, 2015.CrossRefPubMed
29.
Zurück zum Zitat Zhu, H., Yang, Y., Zhu, X., Gui, Z., and Shu, H., General form for obtaining unit disc-based generalized orthogonal moments. IEEE Trans. Image Process. 23(12):5455–5469, 2014.CrossRefPubMed Zhu, H., Yang, Y., Zhu, X., Gui, Z., and Shu, H., General form for obtaining unit disc-based generalized orthogonal moments. IEEE Trans. Image Process. 23(12):5455–5469, 2014.CrossRefPubMed
30.
Zurück zum Zitat Wünsche, A., Generalized Zernike or disc polynomials. J. Comput. Appl. Math. 174(1):135–163, 2005.CrossRef Wünsche, A., Generalized Zernike or disc polynomials. J. Comput. Appl. Math. 174(1):135–163, 2005.CrossRef
31.
Zurück zum Zitat Jing, H., Yang, Y., and Nishikawa, R. M., Retrieval boosted computer-aided diagnosis of clustered microcalcifications for breast cancer. Med. Phys. 39(2):676–685, 2012.CrossRefPubMedPubMedCentral Jing, H., Yang, Y., and Nishikawa, R. M., Retrieval boosted computer-aided diagnosis of clustered microcalcifications for breast cancer. Med. Phys. 39(2):676–685, 2012.CrossRefPubMedPubMedCentral
32.
Zurück zum Zitat Goudas, T., and Maglogiannis, I., An advanced image analysis tool for the quantification and characterization of breast cancer in microscopy images. J. Med. Syst. 39(3):1–13, 2015.CrossRef Goudas, T., and Maglogiannis, I., An advanced image analysis tool for the quantification and characterization of breast cancer in microscopy images. J. Med. Syst. 39(3):1–13, 2015.CrossRef
33.
Zurück zum Zitat Ge, J., Sahiner, B., Hadjiiski, L. M., Chan, L. M., Wei, J., Helvie, M. A., and Zhou, C., Computer aided detection of clusters of microcalcifications on full field digital mammograms. Med. Phys. 33(8):2975–2988, 2006.CrossRefPubMed Ge, J., Sahiner, B., Hadjiiski, L. M., Chan, L. M., Wei, J., Helvie, M. A., and Zhou, C., Computer aided detection of clusters of microcalcifications on full field digital mammograms. Med. Phys. 33(8):2975–2988, 2006.CrossRefPubMed
34.
Zurück zum Zitat Naghibi, S., Teshnehlab, M., and Shoorehdeli, M. A., Breast cancer classification based on advanced multi dimensional fuzzy neural network. J. Med. Syst. 36(5):2713–2720, 2012.CrossRefPubMed Naghibi, S., Teshnehlab, M., and Shoorehdeli, M. A., Breast cancer classification based on advanced multi dimensional fuzzy neural network. J. Med. Syst. 36(5):2713–2720, 2012.CrossRefPubMed
35.
Zurück zum Zitat Lin, K. C., and Hsieh, Y. H., Classification of medical datasets using SVMs with hybrid evolutionary algorithms based on endocrine-based particle swarm optimization and artificial bee colony algorithms. J. Med. Syst. 39(10):1–9, 2015. Lin, K. C., and Hsieh, Y. H., Classification of medical datasets using SVMs with hybrid evolutionary algorithms based on endocrine-based particle swarm optimization and artificial bee colony algorithms. J. Med. Syst. 39(10):1–9, 2015.
36.
Zurück zum Zitat Tsai, M. H., Wang, H. C., Lee, G. W., Lin, Y. C., and Chiu, S. H., A decision tree based classifier to analyze human ovarian cancer cDNA microarray datasets. J. Med. Syst. 40(1):1–8, 2016.CrossRef Tsai, M. H., Wang, H. C., Lee, G. W., Lin, Y. C., and Chiu, S. H., A decision tree based classifier to analyze human ovarian cancer cDNA microarray datasets. J. Med. Syst. 40(1):1–8, 2016.CrossRef
37.
Zurück zum Zitat Dheeba, J., and Selvi, S. T., A swarm optimized neural network system for classification of microcalcification in mammograms. J. Med. Syst. 36(5):3051–3061, 2012.CrossRefPubMed Dheeba, J., and Selvi, S. T., A swarm optimized neural network system for classification of microcalcification in mammograms. J. Med. Syst. 36(5):3051–3061, 2012.CrossRefPubMed
38.
Zurück zum Zitat Dheeba, J., and Selvi, S. T., An improved decision support system for detection of lesions in mammograms using differential evolution optimized wavelet neural network. J. Med. Syst. 36(5):3223–3232, 2012.CrossRefPubMed Dheeba, J., and Selvi, S. T., An improved decision support system for detection of lesions in mammograms using differential evolution optimized wavelet neural network. J. Med. Syst. 36(5):3223–3232, 2012.CrossRefPubMed
39.
Zurück zum Zitat Chao, C. M., Yu, Y. W., Cheng, B. W., and Kuo, Y. L., Construction the model on the breast cancer survival analysis use support vector machine, logistic regression and decision tree. J. Med. Syst. 38(10):1–7, 2014.CrossRef Chao, C. M., Yu, Y. W., Cheng, B. W., and Kuo, Y. L., Construction the model on the breast cancer survival analysis use support vector machine, logistic regression and decision tree. J. Med. Syst. 38(10):1–7, 2014.CrossRef
40.
Zurück zum Zitat Zakeri, F. S., Behnam, H., and Ahmadinejad, N., Classification of benign and malignant breast masses based on shape and texture features in sonography images. J. Med. Syst. 36(3):1621–1627, 2012.CrossRefPubMed Zakeri, F. S., Behnam, H., and Ahmadinejad, N., Classification of benign and malignant breast masses based on shape and texture features in sonography images. J. Med. Syst. 36(3):1621–1627, 2012.CrossRefPubMed
41.
Zurück zum Zitat Dheeba, J., Singh, N. A., and Selvi, S. T., Computer-aided detection of breast cancer on mammograms: a swarm intelligence optimized wavelet neural network approach. J. Biomed. Inform. 49:45–52, 2014.CrossRefPubMed Dheeba, J., Singh, N. A., and Selvi, S. T., Computer-aided detection of breast cancer on mammograms: a swarm intelligence optimized wavelet neural network approach. J. Biomed. Inform. 49:45–52, 2014.CrossRefPubMed
42.
Zurück zum Zitat Zhang, J., Walter, G. G., Miao, Y., and Lee, W. N. W., Wavelet neural networks for function learning. IEEE Trans. Signal Process. 43(6):1485–1497, 1995.CrossRef Zhang, J., Walter, G. G., Miao, Y., and Lee, W. N. W., Wavelet neural networks for function learning. IEEE Trans. Signal Process. 43(6):1485–1497, 1995.CrossRef
43.
Zurück zum Zitat Chauhan, N., Ravi, V., and Chandra, D. K., Differential evolution trained wavelet neural networks: application to bankruptcy prediction in banks. Expert Syst. Appl. 36(4):7659–7665, 2009.CrossRef Chauhan, N., Ravi, V., and Chandra, D. K., Differential evolution trained wavelet neural networks: application to bankruptcy prediction in banks. Expert Syst. Appl. 36(4):7659–7665, 2009.CrossRef
44.
Zurück zum Zitat Vesterstrøm, J., and Thomsen, R., A comparative study of differential evolution, particle swarm optimization, and evolutionary algorithms on numerical benchmark problems. In: Congress on Evolutionary Computation, 2004. CEC2004. Vol. 2, pp. 1980–1987. IEEE, Chicago, 2004. Vesterstrøm, J., and Thomsen, R., A comparative study of differential evolution, particle swarm optimization, and evolutionary algorithms on numerical benchmark problems. In: Congress on Evolutionary Computation, 2004. CEC2004. Vol. 2, pp. 1980–1987. IEEE, Chicago, 2004.
45.
Zurück zum Zitat Wang, L., Zeng, Y., and Chen, T., Back propagation neural network with adaptive differential evolution algorithm for time series forecasting. Expert Syst. Appl. 42(2):855–863, 2015.CrossRef Wang, L., Zeng, Y., and Chen, T., Back propagation neural network with adaptive differential evolution algorithm for time series forecasting. Expert Syst. Appl. 42(2):855–863, 2015.CrossRef
46.
Zurück zum Zitat Suckling, J., Parker, J., Dance, D., Astley, S., Astley, I., Hutt, I., and Boggis, C., The mammographic images analysis society digital mammogram database. Exerpta Med. 1069:375–378, 1994. Suckling, J., Parker, J., Dance, D., Astley, S., Astley, I., Hutt, I., and Boggis, C., The mammographic images analysis society digital mammogram database. Exerpta Med. 1069:375–378, 1994.
47.
Zurück zum Zitat Heath, M., Bowyer, K., Kopans, D., Kegelmeyer, W., Moore, R., Chang, K., and Munishkumaran, S., Current status of the digital database for screening mammography. In: Digital Mammography. Springer, The Netherlands, pp. 457–460, 1998.CrossRef Heath, M., Bowyer, K., Kopans, D., Kegelmeyer, W., Moore, R., Chang, K., and Munishkumaran, S., Current status of the digital database for screening mammography. In: Digital Mammography. Springer, The Netherlands, pp. 457–460, 1998.CrossRef
48.
Zurück zum Zitat Metz, C. E., Quantification of failure to demonstrate statistical significance: the usefulness of confidence intervals. Investig. Radiol. 28(1):59–63, 1993.CrossRef Metz, C. E., Quantification of failure to demonstrate statistical significance: the usefulness of confidence intervals. Investig. Radiol. 28(1):59–63, 1993.CrossRef
Metadaten
Titel
An Improved CAD System for Breast Cancer Diagnosis Based on Generalized Pseudo-Zernike Moment and Ada-DEWNN Classifier
verfasst von
Satya P. Singh
Shabana Urooj
Publikationsdatum
01.04.2016
Verlag
Springer US
Erschienen in
Journal of Medical Systems / Ausgabe 4/2016
Print ISSN: 0148-5598
Elektronische ISSN: 1573-689X
DOI
https://doi.org/10.1007/s10916-016-0454-0

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