Abstract
The aim of this article was to provide early detection of breast cancer by using both mammography and histopathology images of the same patient. When the studies in the literature are examined, it is seen that the mammography and histopathology images of the same patient are not used together for early diagnosis of breast cancer. Mammographic and microscopic images can be limited when using only one of them for the early detection of the breast cancer. Therefore, multi-modality solutions that give more accuracy results than single solutions have been realized in this paper. 3 × 50 microscopic (histopathology) and 3 × 50 mammography image sets have been taken from Firat University Medicine Faculty Pathology and Radiology Laboratories, respectively. Optimum feature space has been obtained by minimum redundancy and maximum relevance via mutual information method applying to the 3 × 50 microscopic and mammography images. Then, probabilistic values of suspicious lesions in the image for selected features have been found by exponential curve fitting. Jensen Shannon, Hellinger, and Triangle measurements have been used for the diagnosis of breast cancer. It has been proved that these measures have been related to each other. Weight values for selected each feature have been found using these measures. These weight values have been used in object function. Afterward, histopathology and mammography images have been classified as normal, malign, and benign utilizing object function. In the result of this classifier, the accuracy of diagnosis of breast cancer has been estimated probabilistically. Furthermore, classifications have been probabilistically visualized on a pie chart. Consequently, the performances of Jensen Shannon, Hellinger, and Triangle measures have been compared with ROC analysis using histopathology and mammography test images. It has been observed that Jensen Shannon measure has higher performance than Hellinger and Triangle measures. Accuracy rates of histopathology and mammography images in Jensen Shannon measure have been found to 99 and 98 %, respectively.
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Acharya UR et al (2006) Heart rate variability: a review. Med Biol Eng Comput 44(12):1031–1051
American Cancer Society (2009) Global cancer facts and figures. American Cancer Society, Inc., Atlanta
Aytac Korkmaz S, Eren E (2013) Cancer detection in mammograms estimating feature weights via Kullback–Leibler measure. In: 2013 6th international congress on image and signal processing (CISP), vol 2, IEEE, 2013
Başçiftçi F, Eldem A (2013) Using reduced rule base with expert system for the diagnosis of disease in hypertension. Med Biol Eng Comput 51:1287–1293
Campanini R, Dongiovanni D, Iampieri E, Lanconelli N, Masotti M, Palermo G, Riccardi A, Roffilli M (2004) A novel featureless approach to mass detection in digital mammograms based on support vector machines. Phys Med Biol 49(6):961–975
Campos LFA, Silva AC, Barros AK (2005) Diagnosis of breast cancer in digital mammograms using independent component analysis and neural networks. In: X Iberoamerican conference on pattern recognition, Havana. Lecture notes in computer science, vol 3773, pp 460–469
Cha S-H (2007) Comprehensive survey on distance/similarity measures between probability density functions. Int J Math Models Methods Appl Sci 1(4):300–307
Chen HL et al (2012) Support vector machine based diagnostic system for breast cancer using swarm intelligence. J Med Syst 36(4):2505–2519
Cireşan DC et al (2013) Mitosis detection in breast cancer histology images with deep neural networks. In: Medical image computing and computer-assisted intervention–MICCAI 2013. Springer, Berlin, pp 411–418
da Silva JE, Marques de Sá JP, Jossinet J (2000) Classification of breast tissue by electrical impedance spectroscopy. Med Biol Eng Comput 38(1):26–30
Delen D, Walker G, Kadam A (2005) Predicting breast cancer survivability: a comparison of three data mining methods. Artif Intell Med 34(2):113–127
Ding C, Peng HC (2003) Minimum redundancy feature selection from microarray gene expression data. In: Proceedings second IEEE computational systems bioinformatics conference, pp 523–528
Ferlay J, Autier P, Boniol M, Heanue M, Colombet M, Boyle P (2007) Estimates of the cancer incidence and mortality in Europe. Ann Oncol 18(3):581–592
Folke M et al (2003) Critical review of non-invasive respiratory monitoring in medical care. Med Biol Eng Comput 41(4):377–383
García JA, Fdez-Valdivia J, Rodriguez-Sanchez R, Fdez-Vidal XR (2002) Performance of the Kullback–Leibler information gain for predicting image fidelity. In: Proceedings 16th international conference on pattern recognition, vol 3, pp 843–848 IEEE
Guo Y et al (2006) Breast image registration techniques: a survey. Med Biol Eng Comput 44(1–2):15–26
Huang ML, Hung YH et al (2012) Usage of case-based reasoning, neural network and adaptive neuro-fuzzy inference system classification techniques in breast cancer dataset classification diagnosis. J Med Syst 36(2):407–414
Jemal A, Bray F, Center MM, Ferlay J, Ward E, Forman D (2011) Global cancer statistics. CA Cancer J Clin 61(2):69–90
Jossinet J (1996) Variability of impedivity in normal and pathological breast tissue. Med Biol Eng Comput 34(5):346–350
Keles A, Keles A, Yavuz U (2011) Expert system based on neuro fuzzy rules for diagnosis breast cancer. Expert Syst Appl 38(5):5719–5726
Kim KH, Bang SW, Kim SR (2004) Emotion recognition system using short-term monitoring of physiological signals. Med Biol Eng Comput 42(3):419–427
Kim KA, Choi JY, Yoo TK, Kim SK, Chung K, Kim DV (2013) Mortality prediction of rats in acute hemorrhagic shock using machine learning techniques. Med Biol Eng Comput 51:1059–1067
Korkmaz SA, Poyraz M (2014) A new method based for diagnosis of breast cancer cells from microscopic images: DWEE–JHT. J Med Syst 38(9):1–9
Korkmaz SA, Korkmaz MF (2015) A new method based cancer detection in mammogram textures by finding feature weights and using Kullback-Leibler measure with kernel estimation. Opt Int J Light Electron Opt. doi:10.1016/j.ijleo.2015.06.034
Krstovski K, Smith DA, Wallach HM, McGregor A (2013) Efficient nearest-neighbor search in the probability simplex. In: Proceedings of conference on the theory of information retrieval. ACM, p 22
Lee YJ, Mangasarian OL, Wolberg WH (2003) Survival-time classification of breast cancer patients. Comput Optim Appl 25(1–3):151–166
Lee C-H, Fernando G, Dejing D (2011) Calculating feature weights in naive bayes with Kullback–Leibler measure. In: IEEE 11th international conference on data mining (ICDM), IEEE, 2011
Mariani S et al (2012) Efficient automatic classifiers for the detection of A phases of the cyclic alternating pattern in sleep. Med Biol Eng Comput 50:359–372
Martins L, dos Santos A, Silva A, Paiva A (2006) Classification of normal, benign and malignant tissues using co-occurrence matrix and Bayesian neural network in mammographic images. In: Proceedings of the ninth Brazilian symposium on neural networks, pp 479–486
Nahar J, Imam T, Tickle KS, Ali ABMS, Chen Y-PP (2012) Computational intelligence for microarray data and biomedical image analysis for the early diagnosis of breast cancer. Expert Syst Appl 39(16):12371–12377
Nandi RJ et al (2006) Classification of breast masses in mammograms using genetic programming and feature selection. Med Biol Eng Comput 44(8):683–694
Pandey P et al (2014) A comparative and evaluative study of two cytological grading systems in breast carcinoma with histological grading: an important prognostic factor. Anal Cell Pathol 2014:1–6
Penzel T et al (2002) Systematic comparison of different algorithms for apnoea detection based on electrocardiogram recordings. Med Biol Eng Comput 40(4):402–407
Roder D, Houssami N, Farshid G, Gill G, Luke Downey P (2008) Population screening and intensity of screening are associated with reduced breast cancer mortality: evidence of efficacy of mammography screening in Australia. Breast Cancer Res Treat 108(3):409–416
Saritas I (2012) Prediction of breast cancer using artificial neural networks. J Med Syst 36(5):2901–2907
Sengur A (2009) Multiclass least-squares support vector machines for analog modulation classification. Expert Syst Appl 36(3):6681–6685
Sengur A (2012) Support vector machine ensembles for intelligent diagnosis of valvular heart disease. J Med Syst 36(4):2649–2655
Şengür A (2008) An expert system based on principal component analysis, artificial immune system and fuzzy k-NN for diagnosis of valvular heart diseases. Comput Biol Med 38:329–338
Sengur A, Turkoglu I, Cevdet Ince M (2007) Wavelet packet neural networks for texture classification. Expert Syst Appl 32(2):527–533
Shoorehdeli MA (2012) Breast cancer classification based on advanced multi dimensional fuzzy neural network. J Med Syst 36(5):2713–2720
Singh BK, Kesari V, Thoke AS (2015) A dual feature selection approach for classification of breast tumors in ultrasound images using ANN and SVM. Artif Intell Syst Mach Learn 7(3):78–84
Topsøe F (2000) Some inequalities for information divergence and related measures of discrimination. IEEE Trans Inform Theory 44(4):1602–1609
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Korkmaz, S.A., Korkmaz, M.F. & Poyraz, M. Diagnosis of breast cancer in light microscopic and mammographic images textures using relative entropy via kernel estimation. Med Biol Eng Comput 54, 561–573 (2016). https://doi.org/10.1007/s11517-015-1361-0
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DOI: https://doi.org/10.1007/s11517-015-1361-0