Detection and Classification of Breast Cancer Using Machine Learning Techniques for Ultrasound Images

Detection and Classification of Breast Cancer Using Machine Learning Techniques for Ultrasound Images
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© 2022 by IJETT Journal
Volume-70 Issue-3
Year of Publication : 2022
Authors : Akila Victor, Bhuvanjeet Singh Gandhi, Muhammad Rukunuddin Ghalib, Ramani Selvanambi
https://doi.org/10.14445/22315381/IJETT-V70I3P219

How to Cite?

Akila Victor, Bhuvanjeet Singh Gandhi, Muhammad Rukunuddin Ghalib, Ramani Selvanambi, "Detection and Classification of Breast Cancer Using Machine Learning Techniques for Ultrasound Images," International Journal of Engineering Trends and Technology, vol. 70, no. 3, pp. 170-178, 2022. Crossref, https://doi.org/10.14445/22315381/IJETT-V70I3P219

Abstract
One of the most common diseases in the world is cancer. There are many different forms of cancer, and one of the most frequent is breast cancer. Breast cancer can affect anybody, but it most usually affects women. Breast cancer can be cured quickly with early identification and a better knowledge of the illness. A computer-aided diagnostic (CAD) system allows us to uncover several ways to identify and diagnose cancer problems. The primary motivation is for accurate detection to detect cancer as soon as possible. Pre-processing, segmentation, feature extraction, and classification are the four critical steps of detection and identification. Pre-processing techniques employed in this study include median filtering and histogram equalization. For segmentation, a hybrid technique is utilized, and for feature extraction, fundamental methods are applied. For classification, the Support Vector Machine (SVM) is proposed and employed. SVM`s accuracy is then compared to that of other machine learning approaches such as boosted tree (BT), random forest (RF), Naive Bayes (NB), and convolutional neural network (CNN). The results obtained are tabulated, and an accuracy of 93.4% is obtained from the SVM classifier.

Keywords
Ultrasound images, Histogram equalization, Support vector machine, Convolutional neural network, Accuracy.

Reference
[1] J. H. Tanne, Everything you need to know about breast cancer...but were afraid to ask, New York [GNYC], 26 (1993) 52–62.
[2] R. A. Smith, Epidemiology of breast cancer, RSNA Categorical Course Phys., (1994) 21–33.
[3] E. Zelnio, ATR legacy, presented at MSTAR Program Initiation Meeting, Wright Laboratory, Wright-Patterson AFB OH, Sept. (1995).
[4] W. Kegelmeyer, J. M. Pruneda, P. D. Bourland, A. Hillis, M. W. Riggs, and M. L. Nipper, Computer-aided mammographic screening for spiculated lesions, Radiol., (1991) 331–337.
[5] Hui Kong, Metin Gurcan, and Kamel Belkacem-Boussaid, Partitioning Histopathological Images: An Integrated Framework for Supervised Color-Texture Segmentation and Cell Splitting, IEEE Transactions On Medical Imaging, 30(9) (2011) 1661-77
[6] Spanhol, F. A., Oliveira, L. S., Petitjean, C., & Heutte, L., Breast cancer histopathological image classification using convolutional neural networks. In 2016 joint international conference on neural networks (IJCNN), (2016) 2560-2567.IEEE.
[7] Dalle, J. R., Leow, W. K., Racoceanu, D., Tutac, A. E., & Putti, T. C., Automatic breast cancer grading of histopathological images. In 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, (2008) 3052-3055. IEEE.
[8] Veta, M., Pluim, J. P., Van Diest, P. J., & Viergever, M. A., Breast cancer histopathology image analysis: A review. IEEE Transactions on Biomedical Engineering, 61(5) (2014) 1400-1411.
[9] Singh, S., Gupta, P. R., & Sharma, M. K., Breast cancer detection and classification of histopathological images. International Journal of Engineering Science and Technology, 3(5) (2010) 4228.
[10] Nithya, R., & Santhi, B., Classification of normal and abnormal patterns in digital mammograms for diagnosis of breast cancer. International journal of computer applications, 28(6) (2011) 21-25.
[11] Cheng, H. D., Shan, J., Ju, W., Guo, Y., & Zhang, L., Automated breast cancer detection and classification using ultrasound images: A survey. Pattern recognition, 43(1) 299-317.
[12] Schaefer, G., Závišek, M., & Nakashima, T., Thermography based breast cancer analysis using statistical features and fuzzy classification. Pattern Recognition, 42(6) (2009) 1133-1137.
[13] Wolberg, W. H., Street, W. N., & Mangasarian, O. L., Machine learning techniques to diagnose breast cancer from image-processed nuclear features of fine-needle aspirates. Cancer letters, 77(2-3) (1994) 163-171.
[14] Bassett, L. W., & Kimme-Smith, C., Breast sonography. AJR. American journal of roentgenology, 156(3) (1991) 449-455.
[15] Chen, W. M., Chang, R. F., Moon, W. K., & Chen, D. R., Breast cancer diagnosis using three-dimensional ultrasound and pixel relation analysis. Ultrasound in medicine & biology, 29(7) (2003) 1027-1035.
[16] Das, S., & Mohan, A., Medical image enhancement techniques by the bottom hat and median filtering. Int J Electron Commun Comput Eng, 5 (2014) 347-351.
[17] Schmitt, R. M., Meyer, C. R., Carson, P. L., & Samuels, B. I., Median and spatial low?pass filtering in ultrasonic computed tomography. Medical Physics, 11(6) (1984) 767-771.
[18] Arastehfar, S., Pouyan, A. A., & Jalalian, A., An enhanced median filter for removing noise from MR images. Journal of Artificial Intelligence & Data Mining, 1(1) (2013) 13-17.
[19] Cheng, H. D., Shan, J., Ju, W., Guo, Y., & Zhang, L., Automated breast cancer detection and classification using ultrasound images: A survey. Pattern recognition, 43(1) (2010) 299-317.
[20] Thangavel, K., & Karnan, M., Computer-aided diagnosis in digital mammograms: detection of microcalcifications by metaheuristic algorithms. VIP Journal, 5(7) (2005) 41-55.
[21] Chan, H. P., Doi, K., Galhotra, S., Vyborny, C. J., MacMahon, H., & Jokich, P. M., Image feature analysis and computer?aided diagnosis in digital radiography. I. Automated detection of microcalcifications in mammography. Medical Physics, 14(4) (1987) 538-548.
[22] Sundaram, K. M., Sasikala, D., & Rani, P. A., A study on pre-processing a mammogram image using Adaptive Median Filter. International Journal of Innovative Research in Science, Engineering and Technology, 3(3) (2014) 10333-10337.
[23] Kumar, P., & Vijayakumar, B., Brain tumour Mr image segmentation and classification using PCA and RBF kernel-based support vector machine. Middle-East Journal of Scientific Research, 23(9) (2015) 106-2116.
[24] Anitha, V., & Murugavalli, S., Brain tumour classification using a two-tier classifier with adaptive segmentation technique. IET computer vision, 10(1) (2016) 9-17.
[25] Viji, K. A., & Jayakumari, J., Performance evaluation of standard image segmentation methods and clustering algorithms for segmentation of MRI brain tumour images. European Journal of Scientific Research, 79(2) (2012) 166-179.
[26] Jalalian, A., Mashohor, S. B., Mahmud, H. R., Saripan, M. I. B., Ramli, A. R. B., & Karasfi, B., Computer-aided detection/diagnosis of breast cancer in mammography and ultrasound: a review. Clinical imaging, 37(3) (2013) 420-426.
[27] Roy, S., Bhattacharyya, D., Bandyopadhyay, S. K., & Kim, T. H. ., Artefacts and skull stripping: an application towards the pre-processing for brain abnormalities detection from MRI. Int J Control Autom SERSC, 10(4) (2017) 147-160.
[28] Ancy, C. A., & Nair, L. S., An efficient CAD for detection of tumours in mammograms using SVM. In 2017 International Conference on Communication and Signal Processing (ICCSP), (2017) 1431-1435, IEEE.
[29] Sandabad, S., Benba, A., Tahri, Y. S., & Hammouch, A., Novel extraction and tumour detection method using histogram study and SVM classification. International Journal of Signal and Imaging Systems Engineering, 9(4-5) (2016) 202-208.
[30] Lewis, S. H., & Dong, A., Detection of breast tumour candidates using marker-controlled watershed segmentation and morphological analysis. In 2012 IEEE Southwest Symposium on Image Analysis and Interpretation , (2012) 1-4. IEEE.
[31] Filipczuk, P., Kowal, M., & Obuchowicz, A., Multi-label fast marching and seeded watershed segmentation methods for diagnosis of breast cancer cytology. In 2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), (2013) 7368-7371.IEEE.
[32] Hefnawy, A., An improved approach for breast cancer detection in mammograms based on watershed segmentation. International Journal of Computer Applications, 75(15) (2013).
[33] Al-Tarawneh, M. S., Lung cancer detection using image processing techniques. Leonardo Electronic Journal of Practices and Technologies, 11(21) (2012) 147-58.
[34] Jaffery, Z. A., & Singh, L., Performance analysis of image segmentation methods for the detection of masses in mammograms. International Journal of Computer Applications, 82(2) (2013).
[35] Victor, A., Roselin, J., & Kavitha, V., Preferential image segmentation using j segmentation based on colour, shape and texture. International Journal of Engineering and Technology, 2(2) (2010) 131-135.
[36] Zhen Yu Chan., JSEG - Unsupervised Segmentation of Color-Texture Regions in Images (www.mathworks.com/matlabcentral/fileexchange/64123-jseg-unsupervised-segmentation-of-color-texture-regions-in-images), MATLAB Central File Exchange. Retrieved February 27, (2020).
[37] Lugli, Luciano & Tronco, Mario & Porto, Vieira., JSEG Algorithm and Statistical ANN Image Segmentation Techniques for Natural Scenes. 10.5772/14622, (2011).
[38] Verma, B., & Zakos, J., A computer-aided diagnosis system for digital mammograms based on fuzzy-neural and feature extraction techniques. IEEE transactions on information technology in biomedicine, 5(1) (2001) 46-54.
[39] Wroblewska, A., Boninski, P., Przelaskowski, A., & Kazubek, M., Segmentation and feature extraction for reliable classification of microcalcifications in digital mammograms. Optoelectronics Review, (3) (2003) 227-236.
[40] Llobet, R., Paredes, R., & Pérez-Cortés, J. C., Comparison of feature extraction methods for breast cancer detection. In Iberian Conference on Pattern Recognition and Image Analysis, Springer, Berlin, Heidelberg, (2005) 495-502.
[41] Victor, A., & Ghalib, M. R., Detection of Skin Cancer Cells-A Review. Research Journal of Pharmacy and Technology, 10(11) (2017) 4093-4098.
[42] Victor, A., & Ghalib, M., Automatic detection and classification of skin cancer. International Journal of Intelligent Engineering and Systems, 10(3) (2017) 444-451.
[43] Victor, A., & Ghalib, M. R., A hybrid segmentation approach for detection and classification of skin cancer, (2017).
[44] Azadeh NH, Adel A, Afsaneh NH. Comparing the performance of various filters on skin cancer images. International Conference on Robot PRIDE 2013-2014- Medical and Rehabilitation Robotics and Instrumentation 2013-2014 Procedia. Computer Science, 42 (2014) 32-37.
[45] Abdul JJ, Sibi S, Aswin RB. Artificial neural network-based detection of skin cancer. Int J Adv Res Electr Electron Instr Eng, 1 (2012) 200-205.
[46] Mariam A, Mai SM, Amr S. Automatic detection of melanoma skin cancer using texture analysis. Int J Comp Appl, 42 (2012).
[47] Silveira M, Nascimento JC, Marques JS. Comparison of segmentation methods for melanoma diagnosis in dermoscopy images. IEEE J Signal Proc, 3 (2009) 35-45.
[48] Chiem A, Al-Jumaily A, Khushaba RN. A novel hybrid system for skin lesion detection. Proceedings of the 3rd International Conference on Intelligent Sensors, Sensor Networks, and Information Processing, (2007) 567-572.
[49] Emre Celebi M, Hassan AK, Bakhtiyar U. A methodological approach to the classification of dermoscopy images. Comp Med Imag Graph , 31 (2007) 362-373.
[50] J.C. Perez-Cortes, J. Arlandis, and R. Llobet. Fast and accurate handwritten character recognition using approximate nearest neighbours search on large databases. In Workshop on Statistical Pattern Recognition SPR-2000, volume 1876 of Lecture Notes in Artificial Intelligence, (2000) 767–776, Alicante (Spain).
[51] J. Suckling and J. Parker et al. The mammographic images analysis society digital mammogram database. In Excerpta Medica. International Congress Series, 1069 (1994) 375–378.
[52] G.M. te Brake and N. Karssemeijer. Automated detection of breast carcinomas that were not detected in a screening program. Radiology, 207 (1998) 465–471.
[53] M. Wallis and M. Walsh et al. A review of false-negative mammography in an asymptomatic population. Clin Radiol, 44 (1991) 13–15.
[54] Nidhi Mongoriya, Vinod Patel., Review The Breast Cancer Detection Technique Using Hybrid Machine Learning. IJETT International Journal of Computer Science and Engineering 8(6) (2021) 5-8.
[55] Brotobor Deliverance, Edeawe Isaac Osahogie, Brotobor Onoriode.,Awareness And Practice of Student Nurses on Breast Self Examination: A Risk Assessment Tool For Breast Cancer. IJETT International Journal of Nursing and Health Science 6(1) (2020) 66-69.
[56] Dharampal Singh., Review on Breast Cancer. IJETT International Journal of Humanities and Social Science 7(4) (2020) 31-37. 10.14445/23942703/IJHSS-V7I4P106
[57] B.Johnson, P.Keerthi Vasan, V.Thillaivendan., Performance of Hyperthermia for Breast Cancer. IJETT International Journal of Applied Physics 3(2) (2016) 1-5.
[58] Mariam Shadan, Nazoora Khan, Mohammad Amanullah Khan, Hena Ansari and Sufian Zaheer., Histological categorization of stromal desmoplasia in breast cancer and its diagnostic and prognostic utility. IJETT International Journal of Medical Science 4(6) (2017) 18-11.