Call for a Computer-Aided Cancer Detection and Classification Research Initiative in Oman

  • Mirzal, Andri (Department of Computer Science, College of Arts and Applied Sciences, Dhofar University) ;
  • Chaudhry, Shafique Ahmad (Department of Computer Science, College of Arts and Applied Sciences, Dhofar University)
  • Published : 2016.05.01


Cancer is a major health problem in Oman. It is reported that cancer incidence in Oman is the second highest after Saudi Arabia among Gulf Cooperation Council countries. Based on GLOBOCAN estimates, Oman is predicted to face an almost two-fold increase in cancer incidence in the period 2008-2020. However, cancer research in Oman is still in its infancy. This is due to the fact that medical institutions and infrastructure that play central roles in data collection and analysis are relatively new developments in Oman. We believe the country requires an organized plan and efforts to promote local cancer research. In this paper, we discuss current research progress in cancer diagnosis using machine learning techniques to optimize computer aided cancer detection and classification (CAD). We specifically discuss CAD using two major medical data, i.e., medical imaging and microarray gene expression profiling, because medical imaging like mammography, MRI, and PET have been widely used in Oman for assisting radiologists in early cancer diagnosis and microarray data have been proven to be a reliable source for differential diagnosis. We also discuss future cancer research directions and benefits to Oman economy for entering the cancer research and treatment business as it is a multi-billion dollar industry worldwide.


Cancer incidence;cancer research in Oman;cancer detection and classification;medical images


  1. Adham S, Al-Ajmi A, Al-Mandhari M, et al (2014). Validation of a potential biomarker for the diagnosis/treatment of metastatic breast cancer within omani females. TRC Open Research Grant Proposal.
  2. Al-Hamdan N, Ravichandran K, Al-Sayyad J, et al (2009). Incidence of cancer in Gulf Cooperation Council countries, 1998-2001. East Mediterr Health J, 15, 600-11.
  3. Alharrasi AS, AlZadjali F, Guillemin GJ, et al (2014). Exploration of new synthetically modified derivatives of acetyl 11-keto-bhetaboswellic acid (AKBA) isolated from Omani frankincense as tumor sensitizing agents. TRC Open Research Grant Proposal.
  4. Al-Hinai SS, Al-Busaidi AS, Al-Busaidi IH (2011). Medical tourism abroad. Sultan Qaboos Univ Med J, 11, 477-84.
  5. Al-Madouj AN, Eldali A, Al-Zahrani AS (2011). Ten-year cancer incidence among nationals of the GCC states 1998-2007. Gulf Center for Cancer Control and Prevention.
  6. Al-Mahrouqi H, Parkin L, Sharples K (2011). Incidence of stomach cancer in Oman and the Other Gulf Cooperation Council Countries. Oman Med J, 26, 258-62.
  7. Antonie ML (2001). Application of data mining techniques for medical image classification. Proc 2nd International Workshop on Multimedia Data Mining, 94-101.
  8. Appleton B, Talbot H (2006). Globally minimal surfaces by continuous maximal flows. IEEE Transact Pattern Analysis Pattern Recognit, 28, 106-18.
  9. Bayoumi R, AlMarhoon M, AlRiyami N, et al (2013). Validity of different prostate specific markers (total PSA, free PSA, and 2 pro PSA) in the diagnosis of prostate cancer. TRC open research grant proposal.
  10. Bazzani A, Bevilacqua A, Bollini D, et al (2001). An SVM classifier to separate false signals from microcalcifications in digital mammograms. Physics Med Biol, 46, 1651-63.
  11. Boch I (2015). Fuzzy method in medical imaging. handbook of biomedical imaging: methodologies and clinical research, springer science+business media new York, 25-44.
  12. Chabi ML, Borget I, Ardiles R, et al (2012). Evaluation of the accuracy of a computer-aided diagnosis (CAD) system in breast ultrasound according to the radiologist's experience. Academic Radiol, 19, 311-9.
  13. Chambolle A (2005). Total variation minimization and a class of binary MRF models. Energy minimization methods in computer vision and pattern recognition, Springer Berlin Heidelberg, 136-52.
  14. Chan T, Esedoglu S, Nikolova M (2006). Algorithms for finding global minimizers of image segmentation and denoising models. SIAM J Applied Mathematics, 66, 1632-48.
  15. Cohen LD (1991). On active contour models and ballons. Computer Vision, Graphics, and Image Processing: Image Understanding, 53, 211-8.
  16. DDSM (2016),
  17. de Nazare Silva J, de Carvalho Filho AO, Silva AC (2014). Automatic detection of masses in mammograms using quality threshold clustering, correlogram function, and SVM. J Digital Imag, 28, 323-37.
  18. de Oliveira FSS, de Carvalho Filho AO, Silva AC, et al (2015). Classification of breast regions as mass and non-mass based on digital mammograms using taxonomic indexes and SVM. Computers Biol Med, 57, 42-53.
  19. de Souto MCP, Costa IG, de Araujo DSA, et al (2008). Clustering cancer gene expression data: a comparative study. BMC Bioinformatics, 9, 1-14
  20. D'Elia C, Marrocco C, Molinara M, et al (2004). Detection of microcalcificationsclusters in mammograms through TS-MRF segmentation and SVM-based classification. Proceedings of the IEEE 17th International Conference on Pattern Recognit, 742-5.
  21. Dheeba J, Singh NA, Selvi ST (2014). Computer-aided detection of breast cancer on mammograms: A swarm intelligence optimized wavelet neural network approach. J Biomedical Informatics, 49, 45-52.
  22. Duda RO, Hart PE, Stork DG (2001). Pattern Classification, 2nd Edition. Wiley-Interscience.
  23. Eckhouse S, Lewison G, Sullivan R (2008). Trends in the global funding and activity of cancer research. Molecular Oncol, 2, 20-32.
  24. Fonseca P, Mendoza J, Wainer J, et al (2015). Automatic breast density classification using a convolutional neural network architecture search procedure. SPIE Medical Imaging, 941428.
  25. Ganesan K (2013). Computer-aided breast cancer detection using mammograms: A review. IEEE Reviews Biomedical Engineering, 6, 77-98.
  26. Golub TR, Slonim DK, Tamayo P, et al (1999). Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science, 286, 531-537.
  27. Grady L (2005). Multilabel random walker segmentation using prior models. IEEE Conference Computer Vision Pattern Recognition, 763-70.
  28. Guyon I, Weston J, Barnhill S, et al (2002). Gene selection for cancer classification using support vector machines. Machine learning, 46, 389-422.
  29. Haralick RM (1973). Textural features for image classification. IEEE Trans Syst, Man, Cybern, 3, 610-21.
  30. He X, Cai D, Niyogi P (2005). Laplacian score for feature selection. Proc. Advances in Neural Information Processing Systems, 507-17.
  31. Heinlein P, Drexl J, Schneider W (2003). Integrated wavelets for enhancement of microcalcifications in digital mammography. IEEE Trans Med Imag, 22, 402-13.
  32. Hitchman SC, Fong GT (2011). Gender Empowerment and Female-to-Male Smoking Prevalence Ratios. Bulletin World Health Organization, 89, 195-202.
  33. IAEA (2013). Sultanate of Oman faces the growing cancer burden. Division of Programme of Action for Cancer Therapy, International Atomic Energy Agency Vienna International Centre.
  34. IRMA (2016),
  35. Isard M, Blake A (1998). Active contours. Springer-Verlag London Limited.
  36. Jalalian A, Mashohor SB, Mahmud HR, et al (2013). Computer-aided detection/diagnosis of breast cancer in mammography and ultrasound: a review. Clinical Imaging, 37, 420-6.
  37. Jermyn IH, Ishikawa H (2001). Globally optimal regions and boundaries as minimum ratio weight cycles. IEEE Transactions Pattern Analysis Machine Intelligence, 23, 1075-88.
  38. Kamoonpuri H (2013). Foreign treatment. Oman Observer (Sep 16th, 2013).
  39. Karssemeijer N, te Brake GM (1996). Detection of stellate distortions in mammograms. IEEE Trans Med Imag, 15, 611-9.
  40. Kass M, Witkin A, Terzolpoulos D (1988). Snakes: Active contour models. Int J Computer Vision, 1, 321-31.
  41. Kasteng F, Wilking N, Jonsson B (2008). Patient access to cancer drugs in nine countries in the middle east. Final report, Centre for Health Economics, Sweden.
  42. Ke L, Mu N, Kang Y (2010). Mass computer-aided diagnosis method in mammogram based on texture features. Proc 3rd Int Conf Biomedical Engineering Informatics, 354-7.
  43. Khan J, Wei JS, Ringner M, et al (2001). Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks. Nature medicine, 7, 673-9.
  44. Kivanc MM, Kozintsev I, Ramchandran K, et al (1999). Low complexity image denoising based on statistical modelling of wavelet coefficients. IEEE Signal Processing Letters, 6, 300-2.
  45. Kobatake H, Murakami M, Takeo H, et al (1999). Computerized detection of malignant tumors on digital mammograms. IEEE Trans Med Imag, 18, 369-78.
  46. Kolmogorov V, Boykov Y, Rother C (2007). Applications of parametric maxflow in computer vision. International Conference on Computer Vision, 1-8.
  47. Krizek P (2008). Feature selection: stability, algorithms, and evaluation. PhD dissertation, department of cybernetics faculty of electrical engineering, czech technical University.
  48. Liao B, Jiang Y, Liang W, et al (2014). Gene selection using locality sensitive Laplacian score. IEEE/ACM Transactions Computational Biol Bioinformatics, 11, 1146-56.
  49. Ling SC, Abdullah AA, Ahmad WKW (2014). Design of an automated breast cancer masses detection in mammogram using cellular neural network (CNN) algorithm. Advanced Science Letters, 20, 254-8.
  50. Liu X, Tang J (2014). Mass classification in mammograms using selected geometry and texture features, and a new SVM-based feature selection method. IEEE Systems J, 8, 910-20.
  51. Maulik U, Mukhopadhyay A, Chakraborty D (2013). Gene-expression-based cancer subtypes prediction through feature selection and transductive SVM. IEEE Transactions on Biomedical Engineering, 60, 1111-7.
  52. Mehdi I, Monem EA, Al Bahrani BJ, et al (2014). Age at diagnosis of female breast cancer in Oman: Issues and implications. South Asian J Cancer, 3, 101-6.
  53. MIAS (2016)
  54. Mirzal A (2013). NMF based gene selection algorithm for improving performance of the spectral cancer clustering. Proc. 2013 IEEE International Conference on In Control System, Computing and Engineering, 74-8.
  55. Mirzal A (2014a). Nonparametric Tikhonov regularized NMF and its application in cancer clustering. IEEE/ACM Transactions on Computational Biol Bioinformatics, 11, 1208-17.
  56. Mirzal A (2014b). Nonparametric orthogonal NMF and its application in cancer clustering. Proc. the First International Conference on Advanced Data and Information Engineering, 177-84.
  57. Mirzal A (2014c). SVD based gene selection algorithm. Proc. the First International Conference on Advanced Data and Information Engineering, 223-30.
  58. Moore MA (2013). Overview of Cancer Registration Research in the Asian Pacific from 2008-2013. Asian Pac J Cancer Prevent, 14, 4461-84.
  59. Mortensen EN, Barrett WA (1998). Interactive segmentation with intelligent scissors. Graphical Models and Image Processing, 60, 349-84.
  60. Mousa R, Munib Q, Moussa A (2005). Breast cancer diagnosis system based on wavelet analysis and fuzzy-neural. Expert Systems With Applicat, 28, 713-23.
  61. Mudigonda NR, Rangayyan RM, Desautels JEL (2000). Gradient and texture analysis for the classification of mammographic masses. IEEE Trans Med Imag, 19, 1032-43.
  62. Muscat Daily (2014). Report says 25% rise in cancer cases in Oman. Muscat Daily (Feb 6th, 2014).
  63. Nie F, Xiang S, Jia Y et al (2008). Trace ratio criterion for feature selection. Proc. 23rd National Conference on Artificial Intelligence, 671-6.
  64. Nooyi SC, Al-Lawati JA (2011). Cancer incidence in Oman, 1998-2006. Asian Pac J Cancer Prev, 12, 1735-8.
  65. Oman Observer (2014). Anti-cancer Activity of Plant Grown in Oman. Oman Observer (Feb 6th, 2014).
  66. Papadopoulos A, Fotiadis DI, Likas A (2005). Characterization of clustered microcalcifications in digitized mammograms using neural networks and support vector machines. Artificial Intelligence Med, 34, 141-50.
  67. Ramaswamy S, Tamayo P, Rifkin R, et al (2001). Multiclass cancer diagnosis using tumor gene expression signatures. Proc National Academy of Sciences, 98, 15149-54.
  68. Ramteke RJ, Khachane MY (2012). Automatic medical image classification and abnormality detection using k-nearest neighbour. Int J Advanced Computer Res, 2, 190-6.
  69. Ren J (2012). ANN vs. SVM: Which one performs better in classification of MCCs in mammogram imaging. Knowledge-Based Systems, 26, 144-53.
  70. Roayaei JA, Varma S, Reinhold W, et al (2013). A microarray analysis for differential gene expression using bayesian clustering algorithm: Support vector machines (SVMs) to investigate prostate cancer genes. J Computational Biol, 5, 15-22.
  71. Sampat MP, Markey MK, Bovik AC (2005). Computer-aided detection and diagnosis in mammography. Handbook of Image and Video Processing, 1195-217.
  72. Scharcanski J, Jung CR (2006). Denoising and enhancing digital mammographic images for visual screening. Computerized Medical Imag Graphics, 30, 243-54.
  73. Shaji B, Purushothaman S, and Rajeswari R (2013). Implementation of wavelet transform and back propagation neural network for identification of microcalcification in breast. Data Mining and Knowledge Engineering, 5, 361-5.
  74. Shi J, Malik J (2000). Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22, 888-905.
  75. Sikonja MR, Kononenko I (2003). Theoretical and empirical analysis of Relief and ReliefF. Machine Learning, 53, 23-69.
  76. Simoncelli EP, Adelson EH (1996). Noise removal via Bayesian wavelet coring. Proc Int Conf Image Processing, 379-82.
  77. Singh S, Kumar V, Verma HK, et al (2006). SVM based system for classification of microcalcifications in digital mammograms. Proc 28th Annual International Conference in Engineering in Medicine and Biology Society, 4747-50.
  78. Soltanian-Zadeh H, Rafiee-Rad F (2004). Comparison of multiwavelet, wavelet, Haralick, and shape features for microcalcification classification in mammograms. Pattern Recognit, 37, 1973-86.
  79. Song L, Smola A, Gretton A (2012). Feature selection via dependence maximization. J Machine Learning Research, 13, 1393-434.
  80. Times of Oman (2014). Oman may see spurt in identified cancer cases. (Feb 16th, 2014).
  81. Timp S, Varela C, Karssemeijer N (2007). Temporal change analysis forcharacterization of mass lesions in mammography. IEEE Trans Med Imag, 26, 945-53.
  82. Torrents-Barrena J, Puig D, Melendez J, et al (2015). Computer-aided diagnosis ofbreast cancer via Gabor wavelet bank and binary-class SVM in mammographic images. J Experimental & Theoretical Artificial Intelligence, 26, 1-17.
  83. Tsochatzidis L, Zagoris K, Savelonas M, et al (2014). SVM-based CBIR of Breast Masses on Mammograms. Proc. 3rd International Workshop on Artificial Intelligence and Assistive Med, 26-30.
  84. Varshavsky R, Gottlieb A, Linial M, et al (2006). Novel unsupervised feature filtering of biological data. Bioinformatics, 22, 507-13.
  85. Wang S, Siskind JM (2003). Image segmentation with ratio cut. IEEE Transactions on Pattern Analysis and Machine Intelligence, 25, 675-90.
  86. Xie W, Ma Y, Li Y (2015). Breast mass segmentation in digital mammography based onpulse coupled neural network and level set method. Proc. SPIE 9501, Satellite Data Compression, Communications, and Processing XI, 95010J.
  87. Yasar H, Ceylan M (2015). An approach for tissue density classification in mammographic images using artificial neural network based on wavelet and curvelet transforms. Proc. SPIE 9443, Sixth International Conference on Graphic and Image Processing, 94430O.
  88. Yezzi A, Kichenassamy S, Kumar A, et al (1997). A geometric snake model for segmentation of medical imagery. IEEE Transactions on Medical Imaging, 16, 199-209.
  89. Zhao Z, Liu H (2007). Spectral feature selection for supervised and unsupervised learning. Proc. 24th International Conference on Machine Learning, 1151-7.
  90. Zhao Z, Wang L, Liu H, et al (2013). On similarity preserving feature selection. IEEE Transaction on Knowledge and Data Engineering, 25, 619-32.
  91. Zheng Y, Wang Y, Keller BM, et al (2013). A fully-automated software pipeline for integrating breast densityand parenchymal texture analysis for digital mammograms: parameter optimization in a case-control breast cancer risk assessment study. SPIE Medical Imaging, 86701.