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Automated mammogram breast cancer detection using the optimized combination of convolutional and recurrent neural network

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Abstract

The objective of this study is to frame mammogram breast detection model using the optimized hybrid classifier. Image pre-processing, tumor segmentation, feature extraction, and detection are the functional phases of the proposed breast cancer detection. A median filter eliminates the noise of the input mammogram. Further, the optimized region growing segmentation is carried out for segmenting the tumor from the image and the optimized region growing depends on a hybrid meta-heuristic algorithm termed as firefly updated chicken based CSO (FC-CSO). To the next of tumor segmentation, feature extraction is done, which intends to extract the features like grey level co-occurrence matrix (GLCM), and gray level run-length matrix (GRLM). The two deep learning architectures termed as convolutional neural network (CNN), and recurrent neural network (RNN). Moreover, both GLCM and GLRM are considered as input to RNN, and the tumor segmented binary image is considered as input to CNN. The result of this study shows that the AND operation of two classifier output will tend to yield the overall diagnostic accuracy, which outperforms the conventional models.

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Abbreviations

CSO:

Chicken swarm optimization

CAD:

Computer-aided diagnosis

CNN:

Convolutional neural network

FF:

Firefly

FC-CSO:

Firefly updated chicken-based CSO

FPR:

False positive rate

GLRM:

Gray-level run-length matrix

MCC:

Mathews correlation coefficient

RNN:

Recurrent neural network

MRI:

Magnetic resonance imaging

ELM:

Extreme learning machine

PDEs:

Partial differential equations

SDEs:

Simple differential equations

EINP:

Estimation of intensity via non-parametric approach

IMBC:

Incoherent motion in breast cancer

ADEWNN:

Adaptive differential evolution wavelet neural network

MIAS:

Mammographic image analysis society

MLP:

Multi-layer perceptrons

PCET:

Polar complex exponential transform

SVM:

Support vector machines

IRMA:

Image retrieval in medical applications

SURF:

Speed-up robust features

DNN:

Deep neural network

FNR:

False-positive rate

MSVM:

Multiclass support vector machine

LASSO:

Least absolute shrinkage and selection operator

DL-CNN:

Deep learning-convolution neural network

RBF:

Radial basis functions

HGRE:

High grey level run emphasis

ANN:

Artificial neural network

LSTM:

Long short-term memory

GRU:

Gate recurrent unit

NPV:

Negative predictive value

LGRE:

Low grey level run emphasis

CSA:

Crow search algorithm

FDR:

False discovery rate

GLCM:

Grey level co-occurrence matrix

IAP-CSA:

Improved awareness probability-based chicken swarm optimization

WCO:

World cup optimization

References

  1. Siegel RL, Miller KD, Fedewa SA, Ahnen DJ, Meester RGS, Barzi A, Jemal A (2017) Colorectal cancer statistics, 2017. CA Cancer J Clin 67(3):177–193

    Article  Google Scholar 

  2. Sapate S, Talbar S, Mahajan A, Sable N, Desai S, Thakur M (2019) Breast cancer diagnosis using abnormalities on ipsilateral views of digital mammograms. Biocybern Biomed Eng 40:290–305

    Article  Google Scholar 

  3. Pawar MM, Talbar SN (2016) Genetic fuzzy system (GFS) based wavelet co-occurrence feature selection in mammogram classification for breast cancer diagnosis. Perspect Sci 8:247–250

    Article  Google Scholar 

  4. Raghavendra U, Acharya UR, Fujita H, Gudigar A, Tan JH, Chokkadi S (2016) Application of gabor wavelet and locality sensitive discriminant analysis for automated identification of breast cancer using digitized mammogram images. Appl Soft Comput 46:151–161

    Article  Google Scholar 

  5. Ji Z, Lou C, Yang S, Xing D (2012) Three-dimensional thermoacoustic imaging for early breast cancer detection. Med Phys 39(11):6738–6744

    Article  Google Scholar 

  6. Mohanty AK, Senapati MR, Lenka SK (2016) Retraction note to an improved data mining technique for classification and detection of breast cancer from mammograms. Neural Comput Appl 27(1):249–249

    Article  Google Scholar 

  7. Timmers JM, van Doorne-Nagtegaal HJ, Zonderland HM, van Tinteren H, Visser O, Verbeek AL, den Heeten GJ, Broeders MJ (2012) The breast imaging reporting and data system (BI-RADS) in the Dutch breast cancer screening programme: its role as an assessment and stratification tool. Eur Radiol 22(8):1717–1723

    Article  Google Scholar 

  8. Al-antari MA, Al-masni MA, Park S-U, Park JH, Metwally MK, Kadah YM, Han S-M, Kim T-S (2018) An automatic computer-aided diagnosis system for breast cancer in digital mammograms via deep belief network. J Med Biol Eng 38(3):443–456

    Article  Google Scholar 

  9. Ogiela MR, Krzyworzeka N (2016) Heuristic approach for computer-aided lesion detection in mammograms. Soft Comput 20(10):4193–4202

    Article  Google Scholar 

  10. Bhateja V, Misra M, Urooj S, Lay-Ekuakille A (2013) A robust polynomial filtering framework for mammographic image enhancement from biomedical sensors. IEEE Sens J 13(11):4147–4156

    Article  Google Scholar 

  11. Bhateja V, Patel H, Krishn A, Sahu A, Lay-Ekuakille A (2015) Multimodal medical image sensor fusion framework using cascade of wavelet and contourlet transform domains. IEEE Sens J 15(12):6783–6790

    Article  Google Scholar 

  12. Bai PR, Liu QY, Li L, Teng SH, Li J, Cao MY (2013) A novel region-based level set method initialized with mean shift clustering for automated medical image segmentation. Comput Biol Med 43(11):1827–1832

    Article  Google Scholar 

  13. Li X, Radulovic M, Kanjer K, Plataniotis KN (2019) Discriminative pattern mining for bBreast cancer histopathology image classification via fully convolutional autoencoder. IEEE Access 7:36433–36445

    Article  Google Scholar 

  14. Wei D, Weinstein S, Hsieh M, Pantalone L, Kontos D (2019) Three-dimensional whole breast segmentation in sagittal and axial breast MRI with dense depth field modeling and localized self-adaptation for chest-wall line detection. IEEE Trans Biomed Eng 66(6):1567–1579

    Article  Google Scholar 

  15. Chiang T, Huang Y, Chen R, Huang C, Chang R (2019) Tumor detection in automated breast ultrasound using 3-D CNN and prioritized candidate aggregation. IEEE Trans Med Imaging 38(1):240–249

    Article  Google Scholar 

  16. Wang X-F, Min H, Zou L, Zhang Y-G (2015) A novel level set method for image segmentation by incorporating local statistical analysis and global similarity measurement. Pattern Recognit 48(1):189–204

    Article  Google Scholar 

  17. Shin H-C, Roth HR, Gao M, Lu L, Xu Z, Nogues I, Yao J, Mollura D, Summers RM (2016) Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics, and transfer learning. IEEE Trans Med Imaging 35(5):1285–1298

    Article  Google Scholar 

  18. Wang Z, Li M, Wang H, Jiang H, Yao Y, Zhang H, Xin J (2019) Breast cancer detection using extreme learning machine based on feature fusion with CNN deep features. IEEE Access 7:105146–105158

    Article  Google Scholar 

  19. Geweid GGN, Abdallah MA (2019) A novel approach for breast cancer investigation and recognition using M-level set-based optimization functions. IEEE Access 7:136343–136357

    Article  Google Scholar 

  20. Singh SP, Urooj S, Lay-Ekuakille A (2016) Breast cancer detection using PCPCET and ADEWNN: a geometric invariant approach to medical X-ray image sensors. IEEE Sens J 16(12):4847–4855

    Article  Google Scholar 

  21. Li H, Zhuang S, Li D-a, Zhao J, Ma Y (2019) Benign and malignant classification of mammogram images based on deep learning. Biomed Signal Process Control 51:347–354

    Article  Google Scholar 

  22. da Cruz TN, da Cruz TM, dos Santos WP (2018) Detection and classification of mammary lesions using artificial neural networks and morphological wavelets. IEEE Lat Am Trans 16(3):926–932

    Article  Google Scholar 

  23. Kaur P, Singh G, Kaur P (2019) Intellectual detection and validation of automated mammogram breast cancer images by multi-class SVM using deep learning classification. Inf Med Unlocked 16:100151

    Article  Google Scholar 

  24. Hai J, Tan H, Chen J, Wu M, Qiao K, Xu J, Zeng L, Gao F, Shi D, Yan B (2019) Multi-level features combined end-to-end learning for automated pathological grading of breast cancer on digital mammograms. Comput Med Imaging Graph 71:58–66

    Article  Google Scholar 

  25. Duraisamy S, Emperumal S (2017) Computer-aided mammogram diagnosis system using deep learning convolutional fully complex-valued relaxation neural network classifier. IET Comput Vis 11(8):656–662

    Article  Google Scholar 

  26. Razmjooy N, Loschi HJ, Estrela VV (2019) A study on metaheuristic-based neural networks for image segmentation purposes. Data Sci. https://doi.org/10.1201/9780429263798-2

    Article  Google Scholar 

  27. Guo G, Razmjooy N (2019) A new interval differential equation for edge detection and determining breast cancer regions in mammography images. Syst Sci Control Eng 7(1):346–356

    Article  Google Scholar 

  28. Razmjooy N, Mousavi BS, Soleymani F, Khotbesara MH (2013) A computer-aided diagnosis system for malignant melanomas. Neural Comput Appl 23(7–8):2059–2071

    Article  Google Scholar 

  29. Razmjooy N, Ashourian M, Karimifard M, Estrela VV, Loschi HJ, do Nascimento D, França RP, Vishnevski M (2020) Computer-aided diagnosis of skin cancer: a review. Curr Med Imaging Rev. https://doi.org/10.2174/1573405616666200129095242

    Article  Google Scholar 

  30. Razmjooy N, Sheykhahmad FR, Ghadimi N (2018) A hybrid neural network–world cup optimization algorithm for melanoma detection. Open Med 13(1):9–16

    Article  Google Scholar 

  31. Zhu Y, Huang C (2012) An improved median filtering algorithm for image noise reduction. Phys Proc 25:609–616

    Article  Google Scholar 

  32. Malegori C, Franzetti L, Guidetti R, Casiraghi E, Rossi R (2016) GLCM, an image analysis technique for early detection of biofilm. J Food Eng 185:48–55

    Article  Google Scholar 

  33. Radhakrishnan M, Kuttiannan T (2012) Comparative analysis of feature extraction methods for the classification of prostate cancer from TRUS medical images. IJCSI Int J Comput Sci Issues 9(1):171–179.

    Google Scholar 

  34. Feng Q, Gao B, Lu P, Woo WL, Yang Y, Fan Y, Qiu X, Gu L (2018) Automatic seeded region growing for thermography debonding detection of CFRP. NDT E Int 99:36–49

    Article  Google Scholar 

  35. Meng X, Liu Y, Gao X, Zhang H (2014) A new bio-inspired algorithm: chicken swarm optimization In: International Conference in Swarm intelligence, pp 86–94

  36. Gandomi AH, Yang X-S, Talatahari S, Alavi AH (2013) Firefly algorithm with chaos. Commun Nonlinear Sci Numer Simul 18(1):89–98

    Article  MathSciNet  Google Scholar 

  37. Li F, Liu M (2019) A hybrid convolutional and recurrent neural network for hippocampus analysis in Alzheimer’s disease. J Neurosci Methods 323:108–118

    Article  Google Scholar 

  38. Askarzadeh A (2016) A novel metaheuristic method for solving constrained engineering optimization problems: crow search algorithm. Comput Struct 169:1–12

    Article  Google Scholar 

  39. Patil RS, Biradar N (2019) Improved region growing segmentation for breast cancer detection: progression of optimized fuzzy classifier. In Communication

  40. Fernández-Navarro F, Carbonero-Ruz M, Alonso DB, Torres-Jiménez M (2017) Global sensitivity estimates for neural network classifiers. IEEE Trans Neural Netw Learn Syst 28(11):2592–2604

    Article  MathSciNet  Google Scholar 

  41. Yu S, Tan KK, Sng BL, Li S, Sia ATH (2015) Lumbar ultrasound image feature extraction and classification with support vector machine. Ultrasound Med Biol 41(10):2677–2689

    Article  Google Scholar 

  42. Sousa MJ, Moutinho A, Almeida M (2019) Classification of potential fire outbreaks: a fuzzy modeling approach based on thermal images. Expert Syst Appl 129:216–232

    Article  Google Scholar 

  43. Namatēvs I (2017) Deep convolutional neural networks: structure feature extraction, and training. Inform Technol Manag Sci 20:40–47

    Google Scholar 

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Authors and Affiliations

  1. Department of Electronics and Communication, B.L.D.E.A, s V.P. Dr. P.G.Halakatti College of Engg. & Tech., (Affiliated toVisvesvaraya Technological University, Belagavi-590018), Vijayapur, Karnataka, 586103, India

    Rajeshwari S. Patil

  2. Department of Electronics and Communication, Bheemanna Khandre Institute of Technology, Bhalki, Karnataka, India

    Nagashettappa Biradar

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  1. Rajeshwari S. Patil
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Correspondence to Rajeshwari S. Patil.

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Patil, R.S., Biradar, N. Automated mammogram breast cancer detection using the optimized combination of convolutional and recurrent neural network. Evol. Intel. 14, 1459–1474 (2021). https://doi.org/10.1007/s12065-020-00403-x

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  • DOI: https://doi.org/10.1007/s12065-020-00403-x

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