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Machine Learning Cancer Diagnosis Based on Medical Image Size and Modalities

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Enabling AI Applications in Data Science

Part of the book series: Studies in Computational Intelligence ((SCI,volume 911))

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Abstract

Nowadays, machine learning (ML) is one of the significant technology in the new era. It almost applies to all the fields in our lives. ML has a notable impact on medical care when dealing with medical images. Medical images are used to investigate the internal-parts of the body of human. The type and size of medical images vary from one scan to another. Besides, medical images are not like natural images. While natural images can perform object detection and classification easily, even if the images are re-sized to smaller images. Resizing medical images is not an efficient method because they interact with pixel-level. In this chapter, medical imaging modalities and histopathology are explained. Furthermore, the best medical image type and size for classification and detection of medical diagnoses are explained. Moreover, specific methods are considered in medical images such as image compression, image format, image resize, and other essential aspects. Finally, we also give a brief summary of deep learning algorithms that are used with medical images.

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References

  1. Fowler, J.F., Hall, E.J.: Radiobiology for the Radiologist. Radiat. Res. 116, 175 (1988)

    Google Scholar 

  2. Mifflin, J.: Visual archives in perspective: enlarging on historical medical photographs. Am. Arch. 70(1), 32–69 (2007)

    Google Scholar 

  3. Cosman, P.C., Gray, R.M., Olshen, R.A.: Evaluating quality of compressed medical images: SNR, subjective rating, and diagnostic accuracy. Proc. IEEE 82(6), 919–32 (1994)

    Google Scholar 

  4. Kayser, K., Görtler, J., Goldmann, T., Vollmer, E., Hufnagl, P., Kayser, G.: Image standards in tissue-based diagnosis (diagnostic surgical pathology). Diagn. Pathol. 3(1), 17 (2008)

    Google Scholar 

  5. Ramakrishna, B., Liu, W., Saiprasad, G., Safdar, N., Chang, C.I., Siddiqui, K., Kim, W., Siegel, E., Chai, J.W., Chen, C.C., Lee, S.K.: An automatic computer-aided detection system for meniscal tears on magnetic resonance images. IEEE Trans. Med. Imaging 28(8), 1308–1316 (2009)

    Google Scholar 

  6. Brenner, D.J., Hall, E.J.: Computed tomography-an increasing source of radiation exposure. New Engl. J. Med. 357(22), 2277–2284 (2007)

    Google Scholar 

  7. Foltz, W.D., Jaffray, D.A.: Principles of magnetic resonance imaging. Radiat. Res. 177(4), 331–348 (2012)

    Google Scholar 

  8. Fass, L.: Imaging and cancer: a review. Molecular oncology. 2(2), 115–52 (2008)

    Google Scholar 

  9. Ehman, R.L., Hendee, W.R., Welch, M.J., Dunnick, N.R., Bresolin, L.B., Arenson, R.L., Baum, S., Hricak, H., Thrall, J.H.: Blueprint for imaging in biomedical research. Radiology 244(1), 12–27 (2007)

    Google Scholar 

  10. Hillman, B.J.: Introduction to the special issue on medical imaging in oncology. J. Clin. Oncol. 24(20), 3223–3224 (2006)

    Google Scholar 

  11. Lehman, C.D., Isaacs, C., Schnall, M.D., Pisano, E.D., Ascher, S.M., Weatherall, P.T., Bluemke, D.A., Bowen, D.J., Marcom, P.K., Armstrong, D.K., Domchek, S.M.: Cancer yield of mammography, MR, and US in high-risk women: prospective multi-institution breast cancer screening study. Radiology 244(2), 381–388 (2007)

    Google Scholar 

  12. de Torres, J.P., Bastarrika, G., Wisnivesky, J.P., Alcaide, A.B., Campo, A., Seijo, L.M., Pueyo, J.C., Villanueva, A., Lozano, M.D., Montes, U., Montuenga, L.: Assessing the relationship between lung cancer risk and emphysema detected on low-dose CT of the chest. Chest 132(6), 1932–1938 (2007)

    Google Scholar 

  13. Nelson, E.D., Slotoroff, C.B., Gomella, L.G., Halpern, E.J.: Targeted biopsy of the prostate: the impact of color Doppler imaging and elastography on prostate cancer detection and Gleason score. Urology 70(6), 1136–1140 (2007)

    Google Scholar 

  14. Kent, M.S., Port, J.L., Altorki, N.K.: Current state of imaging for lung cancer staging. Thorac. Surg. Clin. 14(1), 1–3 (2004)

    Google Scholar 

  15. Fermé, C., Vanel, D., Ribrag, V., Girinski, T.: Role of imaging to choose treatment: Wednesday 5 October 2005, 08:30–10:00. Cancer Imaging. 2005;5(Spec No A):S113

    Google Scholar 

  16. Haugeland J. Artificial Intelligence: The Very Idea. MIT Press (1989)

    Google Scholar 

  17. Fukushima, K.: Neocognitron: a self-organizing neural network model for a mechanism of pattern recognition unaffected by shift in position. Biol. Cybern. 36(4), 193–202 (1980)

    Google Scholar 

  18. Lo, S.C., Lou, S.L., Lin, J.S., Freedman, M.T., Chien, M.V., Mun, S.K.: Artificial convolution neural network techniques and applications for lung nodule detection. IEEE Trans. Med. Imaging 14(4), 711–718 (1995)

    Google Scholar 

  19. LeCun, Y., Bottou, L., Bengio, Y., Haffner, P.: Gradient-based learning applied to document recognition. Proc. IEEE 86(11), 2278–2324 (1998)

    Google Scholar 

  20. Krizhevsky, A., Sutskever, I., Hinton, G.E.: Imagenet classification with deep convolutional neural networks. In: Advances in Neural Information Processing Systems, pp. 1097–1105 (2012)

    Google Scholar 

  21. Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A.C.: Imagenet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211–252 (2015)

    Google Scholar 

  22. Bengio, Y., Courville, A., Vincent, P.: Representation learning: a review and new perspectives. IEEE Trans. Pattern Anal. Mach. Intell. 35(8), 1798–1828 (2013)

    Google Scholar 

  23. Ravì, D., Wong, C., Deligianni, F., Berthelot, M., Andreu-Perez, J., Lo, B., Yang, G.Z.: Deep learning for health informatics. IEEE J. Biomed. Health Inf. 21(1), 4–21 (2016)

    Article  Google Scholar 

  24. Shen, D., Wu, G., Suk, H.I.: Deep learning in medical image analysis. Ann. Rev. Biomed. Eng. 21(19), 221–248 (2017)

    Google Scholar 

  25. Smola, A., Vishwanathan, S.V.: Introduction to Machine Learning, vol. 32, pp. 34. Cambridge University, UK (2008)

    Google Scholar 

  26. Caruana, R.: Multitask learning. Mach. Learn. 28(1), 41–75 (1997)

    Article  MathSciNet  Google Scholar 

  27. Thrun, S.: Is learning the n-th thing any easier than learning the first? In: Advances in Neural Information Processing Systems, pp. 640–646 (1996)

    Google Scholar 

  28. House, D., Walker, M.L., Wu, Z., Wong, J.Y., Betke, M.: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, 2009. CVPR Workshops 2009, pp. 186–193. IEEE (2009)

    Google Scholar 

  29. Kumar, A., Kim, J., Cai, W., Fulham, M., Feng, D.: Content-based medical image retrieval: a survey of applications to multidimensional and multimodality data. J. Digit. Imaging 26(6), 1025–1039 (2013)

    Google Scholar 

  30. Sedghi, S., Sanderson, M., Clough, P.: How do health care professionals select medical images they need? In: Aslib Proceedings. Emerald Group Publishing Limited (29 Jul 2012)

    Google Scholar 

  31. Freeny, P.C., Lawson, T.L.: Radiology of the Pancreas. Springer Science & Business Media (6 Dec 2012)

    Google Scholar 

  32. Anwar, S.M., Majid, M., Qayyum, A., Awais, M., Alnowami, M., Khan, M.K.: Medical image analysis using convolutional neural networks: a review. J. Med. Syst. 42(11), 226 (2018)

    Google Scholar 

  33. Heidenreich, A., Desgrandschamps, F., Terrier, F.: Modern approach of diagnosis and management of acute flank pain: review of all imaging modalities. Eur. Urol. 41(4), 351–362 (2002)

    Google Scholar 

  34. Rahman, M.M., Desai, B.C., Bhattacharya, P.: Medical image retrieval with probabilistic multi-class support vector machine classifiers and adaptive similarity fusion. Comput. Med. Imaging Graph. 32(2), 95–108 (2008)

    Article  Google Scholar 

  35. Sánchez Monedero, J., Saez Manzano, A., Gutiérrez Peña, P.A., Hervas Martínez, C.: Machine learning methods for binary and multiclass classification of melanoma thickness from dermoscopic images. IEEE Trans. Knowl. Data Eng. 2016 (ONLINE)

    Google Scholar 

  36. Miri, M.S., Lee, K., Niemeijer, M., Abràmoff, M.D., Kwon, Y.H., Garvin, M.K.: Multimodal segmentation of optic disc and cup from stereo fundus and SD-OCT images. In: Medical Imaging 2013: Image Processing. International Society for Optics and Photonics, vol. 8669, p. 86690O (13 Mar 2013)

    Google Scholar 

  37. Gao, Y., Zhan, Y., Shen, D.: Incremental learning with selective memory (ILSM): towards fast prostate localization for image guided radiotherapy. IEEE Trans. Med. Imaging 33(2), 518–534 (2013)

    Google Scholar 

  38. Tao, Y., Peng, Z., Krishnan, A., Zhou, X.S.: Robust learning-based parsing and annotation of medical radiographs. IEEE Trans. Med. Imaging. 30(2), 338–350 (2010)

    Google Scholar 

  39. Camlica, Z., Tizhoosh, H.R., Khalvati, F.: Autoencoding the retrieval relevance of medical images. In: 2015 International Conference on Image Processing Theory, Tools and Applications (IPTA), pp. 550–555. IEEE (10 Nov 2015)

    Google Scholar 

  40. Branstetter, B.F.: Practical Imaging Informatics: Foundations and Applications for PACS Professionals. Springer, New York (2009)

    Google Scholar 

  41. Bidgood Jr., W.D., Horii, S.C., Prior, F.W., Van Syckle, D.E.: Understanding and using DICOM, the data interchange standard for biomedical imaging. J. Am. Med. Inf. Assoc. 4(3), 199–212 (1997)

    Google Scholar 

  42. Lauro, G.R., Cable, W., Lesniak, A., Tseytlin, E., McHugh, J., Parwani, A., Pantanowitz, L.: Digital pathology consultations-a new era in digital imaging, challenges and practical applications. J. Digit. Imaging 26(4), 668–677 (2013)

    Google Scholar 

  43. Tirado-Ramos, A., Hu, J., Lee, K.P.: Information object definition-based unified modeling language representation of DICOM structured Reporting: A Case Study of Transcoding DICOM to XML. J. Am. Med. Inf. Assoc. 9(1), 63–72 (2002)

    Article  Google Scholar 

  44. Seibert, J.A.: Modalities and data acquisition. In: Practical Imaging Informatics, pp. 49–66. Springer, New York, NY (2009)

    Google Scholar 

  45. Li, Y., Ping, W.: Cancer metastasis detection with neural conditional random field (19 Jun 2018). arXiv:1806.07064

  46. Cruz-Roa, A., Gilmore, H., Basavanhally, A., Feldman, M., Ganesan, S., Shih, N., Tomaszewski, J., Madabhushi, A., González, F.: High-throughput adaptive sampling for whole-slide histopathology image analysis (HASHI) via convolutional neural networks: application to invasive breast cancer detection. PloS one 13(5) (2018)

    Google Scholar 

  47. Kamnitsas, K., Baumgartner, C., Ledig, C., Newcombe, V., Simpson, J., Kane, A., Menon, D., Nori, A., Criminisi, A., Rueckert, D., Glocker, B.: Unsupervised domain adaptation in brain lesion segmentation with adversarial networks. In: International Conference on Information Processing in Medical Imaging, vol. 25, pp. 597–609. Springer, Cham (2017)

    Google Scholar 

  48. Park, S., Pantanowitz, L., Parwani, A.V.: Digital imaging in pathology. Clin. Lab. Med. 32(4), 557–584 (2012)

    Google Scholar 

  49. Larobina, M., Murino, L.: Medical image file formats. J. Digit. Imaging 27(2), 200–206 (2014)

    Google Scholar 

  50. NIFTI documentation, (Available via website, 2018). https://nifti.nimh.nih.gov/nifti-1/documentation (Cited May 18, 2018)

  51. Robb, R.A., Hanson, D.P., Karwoski, R.A., Larson, A.G., Workman, E.L., Stacy, M.C.: Analyze: a comprehensive, operator-interactive software package for multidimensional medical image display and analysis. Comput. Med. Imaging Graph. 13(6), 433–454 (1989)

    Google Scholar 

  52. MINC software library and tools, (Available via website, 2018). http://www.bic.mni.mcgill.ca/ServicesSoftware/MINC (Cited May 18, 2018)

  53. Ukrit, M.F., Umamageswari, A., Suresh, G.R.: A survey on lossless compression for medical images. Int. J. Comput. Appl. 31(8), 47–50 (2011)

    Google Scholar 

  54. Wikipedia: Encyclopedia of Graphics File Formats, (Available via website, 2019). https://en.wikipedia.org/wiki/Machine-learning (25 March 2019)

  55. Hata, A., Yanagawa, M., Honda, O., Kikuchi, N., Miyata, T., Tsukagoshi, S., Uranishi, A., Tomiyama, N.: Effect of matrix size on the image quality of ultra-high-resolution CT of the lung: comparison of 512x512, 1024x1024, and 2048x2048. Acad. Radiol. 25(7), 869–876 (2018)

    Article  Google Scholar 

  56. Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)

    Google Scholar 

  57. Ledig, C., Theis, L., Huszár, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., Wang, Z., Shi, W.: Photo-realistic single image super-resolution using a generative adversarial network. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4681–4690 (2017)

    Google Scholar 

  58. Wang, X., Yu, K., Wu, S., Gu, J., Liu, Y., Dong, C., Qiao, Y., Change Loy, C.: Esrgan: enhanced super-resolution generative adversarial networks. In: Proceedings of the European Conference on Computer Vision (ECCV), pp. 0–0. 2018

    Google Scholar 

  59. Wang, Z., Chen, J., Hoi, S.C.: Deep learning for image super-resolution: a survey (16 Feb 2019). arXiv:1902.06068

  60. Xia, Q., Ni, J., Kanpogninge, A.J., Gee, J.C.: Searchable public-key encryption with data sharing in dynamic groups for mobile cloud storage. J. UCS 21(3), 440–453 (2015)

    Google Scholar 

  61. Chaabouni, I., Fourati, W., Bouhlel, M.S.: Using ROI with ISOM compression to medical image. Int. J. Comput. Vis. Robot. 6(1–2), 65–76 (2016)

    Article  Google Scholar 

  62. Suruliandi, A., Raja, S.P.: Empirical evaluation of EZW and other encoding techniques in the wavelet-based image compression domain. Int. J. Wavelets, Multiresolution Inf. Process. 13(02), 1550012 (2015)

    Google Scholar 

  63. Ang, B.H., Sheikh, U.U.: Marsono MN. 2-D DWT system architecture for image compression. J. Signal Process. Syst. 78(2), 131–137 (2015)

    Google Scholar 

  64. Shih, F.Y., Wu, Y.T.: Robust watermarking and compression for medical images based on genetic algorithms. Inf. Sci. 175(3), 200–216 (2005)

    Google Scholar 

  65. Doukas, C., Maglogiannis, I.: Region of interest coding techniques for medical image compression. IEEE Eng. Med. Biol. Mag. 26(5), 29–35 (2007)

    Article  Google Scholar 

  66. Hernandez-Cabronero, M., Blanes, I., Pinho, A.J., Marcellin, M.W., Serra-Sagristà, J.: Progressive lossy-to-lossless compression of DNA microarray images. IEEE Signal Process. Lett. 23(5), 698–702 (2016)

    Google Scholar 

  67. Pizzolante, R., Carpentieri, B., Castiglione, A.: A secure low complexity approach for compression and transmission of 3-D medical images. In: 2013 Eighth International Conference on Broadband and Wireless Computing, Communication and Applications, pp. 387–392. IEEE (28 Oct 2013)

    Google Scholar 

  68. Bhavani, S., Thanushkodi, K.G.: Comparison of fractal coding methods for medical image compression. IET Image Process. 7(7), 686–693 (2013)

    Google Scholar 

  69. Castiglione, A., Pizzolante, R., De Santis, A., Carpentieri, B., Castiglione, A., Palmieri, F.: Cloud-based adaptive compression and secure management services for 3D healthcare data. Future Gener. Comput. Syst. 1(43), 120–134 (2015)

    Google Scholar 

  70. Ciznicki, M., Kurowski, K., Plaza, A.J.: Graphics processing unit implementation of JPEG2000 for hyperspectral image compression. J. Appl. Remote Sens. 6(1), 061507 (2012)

    Google Scholar 

  71. Bruylants, T., Munteanu, A., Schelkens, P.: Wavelet based volumetric medical image compression. Signal Process. Image Commun. 1(31), 112–133 (2015)

    Google Scholar 

  72. Pu, L., Marcellin, M.W., Bilgin, A., Ashok, A.: Compression based on a joint task-specific information metric. In: 2015 Data compression conference. IEEE, pp. 467–467 (7 Apr 2015)

    Google Scholar 

  73. Starosolski, R.: New simple and efficient color space transformations for lossless image compression. J. Visual Commun. Image Represent. 25(5), 1056–1063 (2014)

    Google Scholar 

  74. The Adoption of Lossy Image Data Compression for the Purpose of Clinical Interpretation, (Available via website, 2017). https://www.rcr.ac.uk/sites/default/files/docs/radiology/pdf/IT-guidance-LossyApr08.pdf (Cited 15 October 2017)

  75. Wu, X., Li, Y., Liu, K., Wang, K., Wang, L.: Massive parallel implementation of JPEG2000 decoding algorithm with multi-GPUs. In: Satellite Data Compression, Communications, and Processing X. International Society for Optics and Photonics, vol. 9124, pp. 91240S (22 May 2014)

    Google Scholar 

  76. Blinder, D., Bruylants, T., Ottevaere, H., Munteanu, A., Schelkens, P.: JPEG 2000-based compression of fringe patterns for digital holographic microscopy. Opt. Eng. 53(12), 123102 (2014)

    Google Scholar 

  77. Chemak, C., Bouhlel, M.S., Lapayre, J.C.: Neurology diagnostics security and terminal adaptation for PocketNeuro project. Telemed. e-Health. 14(7), 671–678 (2008)

    Google Scholar 

  78. Dewan, M.A., Islam, R., Sharif, M.A., Islam, M.A.: An Approach to Improve JPEG for Lossy Still Image Compression. Computer Science & Engineering Discipline, Khulna University, Khulna 9208

    Google Scholar 

  79. Hara, J.: An implementation of JPEG 2000 interactive image communication system. In: 2005 IEEE International Symposium on Circuits and Systems, pp. 5922–5925. IEEE (23 May 2005)

    Google Scholar 

  80. Supplement 145: Whole Slide Microscopic Image IOD and SOP Classes, (Available via website, 2019). ftp://medical.nema.org/MEDICAL/Dicom/Final/sup145-ft.pdf (Cited 10 March 2019)

  81. BigTIFF: BigTIFF Library, (Available via website, 2019). http://bigtiff.org/ (Cited 12 March 2019)

  82. Liu, F., Hernandez-Cabronero, M., Sanchez, V., Marcellin, M.W., Bilgin, A.: The current role of image compression standards in medical imaging. Information 8(4), 131 (2017)

    Google Scholar 

  83. Farahani, N., Parwani, A.V., Pantanowitz, L.: Whole slide imaging in pathology: advantages, limitations, and emerging perspectives. Pathol. Lab. Med. Int. 7(23–33), 4321 (2015)

    Google Scholar 

  84. Komura, D., Ishikawa, S.: Machine learning methods for histopathological image analysis. Comput. Struct. Biotechnol. J. 1(16), 34–42 (2018)

    Article  Google Scholar 

  85. Liu, Y., Gadepalli, K., Norouzi, M., Dahl, G.E., Kohlberger, T., Boyko, A., Venugopalan, S., Timofeev, A., Nelson, P.Q., Corrado, G.S., Hipp, J.D.: Detecting cancer metastases on gigapixel pathology images (3 Mar 2017). arXiv:1703.02442

  86. Wang, D., Khosla, A., Gargeya, R., Irshad, H., Beck, A.H.: Deep learning for identifying metastatic breast cancer (18 Jun 2016). arXiv:1606.05718

  87. Mungle, T., Tewary, S., Das, D.K., Arun, I., Basak, B., Agarwal, S., Ahmed, R., Chatterjee, S., Chakraborty, C.: MRF ANN: a machine learning approach for automated ER scoring of breast cancer immunohistochemical images. J. Microsc. 267(2), 117–129 (2017)

    Google Scholar 

  88. Wang, D., Foran, D.J., Ren, J., Zhong, H., Kim, I.Y., Qi, X.: Exploring automatic prostate histopathology image gleason grading via local structure modeling. In: 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), pp. 2649–2652. IEEE (25 Aug 2015)

    Google Scholar 

  89. Wollmann, T., Rohr, K.: Automatic breast cancer grading in lymph nodes using a deep neural network (24 Jul 2017). arXiv:1707.07565

  90. Gutman, D., Codella, N.C., Celebi, E., Helba, B., Marchetti, M., Mishra, N., Halpern, A.: Skin lesion analysis toward melanoma detection: a challenge at the international symposium on biomedical imaging (ISBI) 2016, hosted by the international skin imaging collaboration (ISIC) (4 May 2016). arXiv:1605.01397

  91. Erickson, B.J., Korfiatis, P., Akkus, Z., Kline, T.L.: Machine learning for medical imaging. Radiographics 37(2), 505–515 (2017)

    Google Scholar 

  92. Ronneberger, O., Fischer, P., Brox, T.: U-net: convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-assisted Intervention, vol. 5, pp. 234–241. Springer, Cham (2015)

    Google Scholar 

  93. Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 3431–3440 (2015)

    Google Scholar 

  94. LeCun, Y., Bengio, Y.: Convolutional networks for images, speech, and time series. In: The Handbook of Brain Theory and Neural Networks, vol. 3361, no. 10 (1995)

    Google Scholar 

  95. Al-Dhabyani, W., Gomaa, M., Khaled, H., Aly, F.: Deep learning approaches for data augmentation and classification of breast masses using ultrasound images. Int. J. Adv. Comput. Sci. Appl. 10(5) (2019)

    Google Scholar 

  96. Khalifa, N.E., Taha, M.H., Hassanien, A.E., Hemedan, A.A.: Deep bacteria: robust deep learning data augmentation design for limited bacterial colony dataset. Int. J. Reason. Based Intell. Syst. 11(3), 256–64 (2019)

    Google Scholar 

  97. Khalifa, N.E., Taha, M.H., Hassanien, A.E., Mohamed, H.N.: Deep iris: deep learning for gender classification through iris patterns. Acta Informatica Medica 27(2), 96 (2019)

    Google Scholar 

  98. Al-Dhabyani, W., Gomaa, M., Khaled, H., Fahmy, A.: Dataset of breast ultrasound images. Data Brief 1(28), 104863 (2020)

    Google Scholar 

  99. Cancer Imaging Archive, (Available via website, 2018). http://www.cancerimagingarchive.net (Cited 20 October 2018)

  100. National Cancer Institute, Genomic data commons data portal (legacy archive), (Available via website, 2018). https://portal.gdc.cancer.gov/legacy-archive/ (Cited 18 October 2018)

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Acknowledgements

The authors would like to express their gratitude to Mohammed Almurisi for his support.

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  1. Faculty of Computers and Artificial Intelligence, Cairo University, Cairo, Egypt

    Walid Al-Dhabyani & Aly Fahmy

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  1. Walid Al-Dhabyani
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Correspondence to Walid Al-Dhabyani .

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  1. Faculty of Computers and Artificial Intelligence, Cairo University, Giza, Egypt

    Aboul-Ella Hassanien

  2. Faculty of Computers and Artificial Intelligence, Cairo University, Giza, Egypt

    Mohamed Hamed N. Taha

  3. Faculty of Computers and Artificial Intelligence, Cairo University, Giza, Egypt

    Nour Eldeen M. Khalifa

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Al-Dhabyani, W., Fahmy, A. (2021). Machine Learning Cancer Diagnosis Based on Medical Image Size and Modalities. In: Hassanien, AE., Taha, M.H.N., Khalifa, N.E.M. (eds) Enabling AI Applications in Data Science. Studies in Computational Intelligence, vol 911. Springer, Cham. https://doi.org/10.1007/978-3-030-52067-0_9

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