Skip to main content

Deep Learning Algorithms in Medical Image Processing for Cancer Diagnosis: Overview, Challenges and Future

  • 759 Accesses

Part of the Studies in Computational Intelligence book series (SCI,volume 908)

Abstract

Health care sector is entirely different from other industrial sector owing to the value of human life and people gives the highest priority. Medical image processing is a research domain where advance computer-aided algorithms are used for disease prognosis and treatment planning. Machine learning comprises of neural networks and fuzzy logic algorithms that have immense applications in the automation of a process. The deep learning algorithm is a machine learning technique that does not relies on feature extraction unlike classical neural network algorithms. Computer-aided automatic processing is in high demand in the medical field due to the improved accuracy and precision. The coupling of machine learning algorithms with high-performance computing gives promising results in medical image analysis like fusion, segmentation, registration and classification. This chapter proposes the applications of deep learning algorithms in cancer diagnosis specifically in the CT/MR brain and abdomen images, mammogram images, histopathological images and also in the detection of diabetic retinopathy. The overview of deep learning algorithms in cancer diagnosis, challenges and future scope is also highlighted in this work. The pros and cons of various types of deep learning neural network architectures are also stated in this work. The intelligent machines in future will be using the deep learning algorithms for the disease diagnosis, treatment planning and surgery.

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-981-15-6321-8_3
  • Chapter length: 30 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   84.99
Price excludes VAT (USA)
  • ISBN: 978-981-15-6321-8
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   109.99
Price excludes VAT (USA)
Hardcover Book
USD   149.99
Price excludes VAT (USA)
Fig. 3.1
Fig. 3.2
Fig. 3.3
Fig. 3.4
Fig. 3.5
Fig. 3.6
Fig. 3.7
Fig. 3.8
Fig. 3.9
Fig. 3.10
Fig. 3.11
Fig. 3.12
Fig. 3.13
Fig. 3.14
Fig. 3.15
Fig. 3.16

References

  1. W.H. Wolberg, W.N. Street, O.L. Mangasarian, Machine learning techniques to diagnose breast cancer from image-processed nuclear features of fine needle aspirates. Cancer Lett. 77(2–3), 163–171 (1994)

    Google Scholar 

  2. H. Bhavsar, A. Ganatra, A comparative study of training algorithms for supervised machine learning. Int. J. Soft Comput. Eng. (IJSCE) 2(4), 2231–2307 (2012)

    Google Scholar 

  3. J.G. Lee, S. Jun, Y.W. Cho, H. Lee, G.B. Kim, J.B. Seo, N. Kim, Deep learning in medical imaging: general overview. Korean J. Radiol. 18(4), 570–584 (2017)

    Google Scholar 

  4. K. Polat, S. Güneş, Breast cancer diagnosis using least square support vector machine. Digit. Sig. Proc. 17(4), 694–701 (2007)

    Google Scholar 

  5. S. Şahan, K. Polat, H. Kodaz, S. Güneş, A new hybrid method based on fuzzy-artificial immune system and k-NN algorithm for breast cancer diagnosis. Comput. Biol. Med. 37(3), 415–423 (2007)

    Google Scholar 

  6. M.F. Akay, Support vector machines combined with feature selection for breast cancer diagnosis. Expert Syst. Appl. 36(2), 3240–3247 (2009)

    Google Scholar 

  7. I. Maglogiannis, E. Zafiropoulos, I. Anagnostopoulos, An intelligent system for automated breast cancer diagnosis and prognosis using SVM based classifiers. Appl. Intell. 30(1), 24–36 (2009)

    Google Scholar 

  8. A. Osareh, B. Shadgar, Machine learning techniques to diagnose breast cancer, in 2010 5th International Symposium on Health Informatics and Bioinformatics (IEEE, 2010), pp. 114–120

    Google Scholar 

  9. A.C. Tan, D. Gilbert, Ensemble machine learning on gene expression data for cancer classification, in Proceedings of New Zealand Bioinformatics Conference, Te Papa, Wellington, New Zealand, 13–14 Feb 2003

    Google Scholar 

  10. R. Zhang, G.B. Huang, N. Sundararajan, P. Saratchandran, Multicategory classification using an extreme learning machine for microarray gene expression cancer diagnosis. IEEE/ACM Trans. Comput. Biol. Bioinf. 4(3), 485–495 (2007)

    Google Scholar 

  11. S.L. Hsieh, S.H. Hsieh, P.H. Cheng, C.H. Chen, K.P. Hsu, I.S. Lee, Z. Wang, F. Lai, Design ensemble machine learning model for breast cancer diagnosis. J. Med. Syst. 36(5), 2841–2847 (2012)

    Google Scholar 

  12. D. Lavanya, D.K. Rani, Analysis of feature selection with classification: Breast cancer datasets. Indian J. Comput. Sci. Eng. (IJCSE) 2(5), 756–763 (2011)

    Google Scholar 

  13. R. Ramos-Pollán, M.A. Guevara-López, C. Suárez-Ortega, G. Díaz-Herrero, J.M. Franco-Valiente, M. Rubio-Del-Solar, N. González-De-Posada, M.A. Vaz, J. Loureiro, I. Ramos, Discovering mammography-based machine learning classifiers for breast cancer diagnosis. J. Med. Syst. 36(4), 2259–2269 (2012)

    Google Scholar 

  14. K. Rajesh, S. Anand, Analysis of SEER dataset for breast cancer diagnosis using C4. 5 classification algorithm. Int. J. Adv. Res. Comput. Commun. Eng. 1(2), 2278–1021 (2012)

    Google Scholar 

  15. G.I. Salama, M. Abdelhalim, M.A. Zeid, Breast cancer diagnosis on three different datasets using multi-classifiers. Breast Cancer (WDBC) 32(569), 2 (2012)

    Google Scholar 

  16. S. Kharya, Using data mining techniques for diagnosis and prognosis of cancer disease (2012). arXiv preprint arXiv:1205.1923

  17. L.G. Ahmad, A.T. Eshlaghy, A. Poorebrahimi, M. Ebrahimi, A.R. Razavi, Using three machine learning techniques for predicting breast cancer recurrence. J. Health Med. Inform. 4(124), 3 (2013)

    Google Scholar 

  18. Y. Gal, R. Islam, Z. Ghahramani, Deep bayesian active learning with image data, in Proceedings of the 34th International Conference on Machine Learning, vol. 70 (2017), pp. 1183–1192. JMLR.org

    Google Scholar 

  19. S. Liu, H. Zheng, Y. Feng, W. Li, Prostate cancer diagnosis using deep learning with 3D multiparametric MRI, in Medical Imaging 2017: Computer-Aided Diagnosis, vol. 10134 (International Society for Optics and Photonics, 2017), p. 1013428

    Google Scholar 

  20. K. Kuan, M. Ravaut, G. Manek, H. Chen, J. Lin, B. Nazir, C. Chen, T.C. Howe, Z. Zeng, V. Chandrasekhar, Deep learning for lung cancer detection: tackling the kaggle data science bowl 2017 challenge (2017). arXiv preprint arXiv:1705.09435

  21. N. Coudray, P.S. Ocampo, T. Sakellaropoulos, N. Narula, M. Snuderl, D. Fenyö, A.L. Moreira, N. Razavian, A. Tsirigos, Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning. Nat. Med. 24(10), 1559–1567 (2018)

    Google Scholar 

  22. A. Teramoto, T. Tsukamoto, Y. Kiriyama, H. Fujita, Automated classification of lung cancer types from cytological images using deep convolutional neural networks. BioMed Res. Int. 2017 (2017)

    Google Scholar 

  23. Q. Song, L. Zhao, X. Luo, X. Dou, Using deep learning for classification of lung nodules on computed tomography images. J. Healthc. Eng. 2017 (2017)

    Google Scholar 

  24. W. Sun, B. Zheng, W. Qian, Automatic feature learning using multichannel ROI based on deep structured algorithms for computerized lung cancer diagnosis. Comput. Biol. Med. 1(89), 530–539 (2017)

    Google Scholar 

  25. R. Platania, S. Shams, S. Yang, J. Zhang, K. Lee, S.J. Park, Automated breast cancer diagnosis using deep learning and region of interest detection (bc-droid), in Proceedings of the 8th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (2017), pp. 536–543

    Google Scholar 

  26. A.S. Becker, M. Marcon, S. Ghafoor, M.C. Wurnig, T. Frauenfelder, A. Boss, Deep learning in mammography: diagnostic accuracy of a multipurpose image analysis software in the detection of breast cancer. Invest. Radiol. 52(7), 434–440 (2017)

    Google Scholar 

  27. B.E. Bejnordi, M. Veta, P.J. Van Diest, B. Van Ginneken, N. Karssemeijer, G. Litjens, J.A. Van Der Laak, M. Hermsen, Q.F. Manson, M. Balkenhol, O. Geessink, Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. Jama 318(22), 2199–2210 (2017)

    Google Scholar 

  28. N. Antropova, B.Q. Huynh, M.L. Giger, A deep feature fusion methodology for breast cancer diagnosis demonstrated on three imaging modality datasets. Med. Phys. 44(10), 5162–5171 (2017)

    Google Scholar 

  29. J. Schmidhuber, Deep learning in neural networks: an overview. Neural Netw. 1(61), 85–117 (2015)

    Google Scholar 

  30. C.C. Aggarwal, Neural Networks and Deep Learning, vol. 10 (Springer, Berlin, 2018), pp. 978-983

    Google Scholar 

  31. G.C. Pereira, M. Traughber, R.F. Muzic, The role of imaging in radiation therapy planning: past, present, and future. BioMed Res. Int. 2014 (2014)

    Google Scholar 

  32. G. Litjens, T. Kooi, B.E. Bejnordi, A.A. Setio, F. Ciompi, M. Ghafoorian, J.A. Van Der Laak, B. Van Ginneken, C.I. Sánchez, A survey on deep learning in medical image analysis. Med. Image Anal. 1(42), 60–88 (2017)

    Google Scholar 

  33. J. Dai, Y. Li, K. He, J. Sun, R-fcn: object detection via region-based fully convolutional networks, in Advances in Neural Information Processing Systems (2016), pp. 379–387

    Google Scholar 

  34. M.I. Razzak, S. Naz, A. Zaib, Deep learning for medical image processing: overview, challenges and the future, in Classification in BioApps (Springer, Cham, 2018), pp. 323–350

    Google Scholar 

  35. S.K. Zhou, H. Greenspan, D. Shen (eds.), Deep Learning for Medical Image Analysis (Academic Press, 2017)

    Google Scholar 

  36. A. Oliver, A. Odena, C.A. Raffel, E.D. Cubuk, I. Goodfellow, Realistic evaluation of deep semi-supervised learning algorithms, in Advances in Neural Information Processing Systems (2018), pp. 3235–3246

    Google Scholar 

  37. R. Raina, A. Madhavan, A.Y. Ng, Large-scale deep unsupervised learning using graphics processors, in Proceedings of the 26th Annual International Conference on Machine Learning (2009), pp. 873–880

    Google Scholar 

  38. W. Liu, Z. Wang, X. Liu, N. Zeng, Y. Liu, F.E. Alsaadi, A survey of deep neural network architectures and their applications. Neurocomputing 19(234), 11–26 (2017)

    Google Scholar 

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

    Google Scholar 

  40. M.Z. Alom, T.M. Taha, C. Yakopcic, S. Westberg, P. Sidike, M.S. Nasrin, B.C. Van Esesn, A.A. Awwal, V.K. Asari, The history began from alexnet: a comprehensive survey on deep learning approaches (2018). arXiv preprint arXiv:1803.01164

  41. K. Simonyan, A. Zisserman, Very deep convolutional networks for large-scale image recognition (2014). arXiv preprint arXiv:1409.1556

  42. H.T. Mustafa, J. Yang, M. Zareapoor, Multi-scale convolutional neural network for multi-focus image fusion. Image Vis. Comput. 1(85), 26–35 (2019)

    Google Scholar 

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

    MathSciNet  Google Scholar 

  44. C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, A. Rabinovich, Going deeper with convolutions, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2015), pp. 1–9

    Google Scholar 

  45. K. He, X. Zhang, S. Ren, J. Sun, Deep residual learning for image recognition, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2016), pp. 770–778

    Google Scholar 

  46. S. Targ, D. Almeida, K. Lyman, ResNet in ResNet: generalizing residual architectures (2016). arXiv preprint arXiv:1603.08029

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

    Google Scholar 

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

    Google Scholar 

  49. Ö. Çiçek, A. Abdulkadir, S.S. Lienkamp, T. Brox, O. Ronneberger, 3D U-Net: learning dense volumetric segmentation from sparse annotation, in International Conference on Medical Image Computing and Computer-Assisted Intervention (Springer, Cham, 2016), pp. 424–432

    Google Scholar 

  50. Z. Wang, Q. She, T.E. Ward, Generative adversarial networks: a survey and taxonomy (2019). arXiv preprint arXiv:1906.01529

  51. A. Creswell, T. White, V. Dumoulin, K. Arulkumaran, B. Sengupta, A.A. Bharath, Generative adversarial networks: an overview. IEEE Sig. Process. Mag. 35(1), 53–65 (2018)

    Google Scholar 

  52. S. Hochreiter, The vanishing gradient problem during learning recurrent neural nets and problem solutions. Int. J. Uncertainty Fuzziness Knowl. Based Syst. 6(02), 107–116 (1998)

    MATH  Google Scholar 

  53. P. Liu, X. Qiu, X. Huang, Recurrent neural network for text classification with multi-task learning (2016). arXiv preprint arXiv:1605.05101

  54. M. Loey, A. El-Sawy, H. El-Bakry, Deep learning autoencoder approach for handwritten arabic digits recognition (2017). arXiv preprint arXiv:1706.06720

  55. S.A. Thomas, A.M. Race, R.T. Steven, I.S. Gilmore, J. Bunch, Dimensionality reduction of mass spectrometry imaging data using autoencoders, in 2016 IEEE Symposium Series on Computational Intelligence (SSCI) (IEEE, 2016), pp. 1–7

    Google Scholar 

  56. M.A. Keyvanrad, M.M. Homayounpour, A brief survey on deep belief networks and introducing a new object oriented toolbox (DeeBNet) (2014). arXiv preprint arXiv:1408.3264

  57. G.E. Hinton, Deep belief networks. Scholarpedia 4(5), 5947 (2009)

    Google Scholar 

  58. M. Kallenberg, K. Petersen, M. Nielsen, A.Y. Ng, P. Diao, C. Igel, C.M. Vachon, K. Holland, R.R. Winkel, N. Karssemeijer, M. Lillholm, Unsupervised deep learning applied to breast density segmentation and mammographic risk scoring. IEEE Trans. Med. Imaging 35(5), 1322–1331 (2016)

    Google Scholar 

  59. H. Wang, A.C. Roa, A.N. Basavanhally, H.L. Gilmore, N. Shih, M. Feldman, J. Tomaszewski, F. Gonzalez, A. Madabhushi, Mitosis detection in breast cancer pathology images by combining handcrafted and convolutional neural network features. J. Med. Imaging 1(3), 034003 (2014)

    Google Scholar 

  60. M.G. Ertosun, D.L. Rubin, Probabilistic visual search for masses within mammography images using deep learning, in 2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (IEEE, 2015), pp. 1310–1315

    Google Scholar 

  61. N. Dhungel, G. Carneiro, A.P. Bradley, Automated mass detection in mammograms using cascaded deep learning and random forests, in 2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA) (IEEE, 2015), pp. 1–8

    Google Scholar 

  62. J. Arevalo, F.A. González, R. Ramos-Pollán, J.L. Oliveira, M.A. Lopez, Representation learning for mammography mass lesion classification with convolutional neural networks. Comput. Methods Programs Biomed. 1(127), 248–257 (2016)

    Google Scholar 

  63. S. Albarqouni, C. Baur, F. Achilles, V. Belagiannis, S. Demirci, N. Navab, Aggnet: deep learning from crowds for mitosis detection in breast cancer histology images. IEEE Trans. Med. Imaging 35(5), 1313–1321 (2016)

    Google Scholar 

  64. B.Q. Huynh, H. Li, M.L. Giger, Digital mammographic tumor classification using transfer learning from deep convolutional neural networks. J. Med. Imaging 3(3), 034501 (2016)

    Google Scholar 

  65. R. Turkki, N. Linder, P.E. Kovanen, T. Pellinen, J. Lundin, Antibody-supervised deep learning for quantification of tumor-infiltrating immune cells in hematoxylin and eosin stained breast cancer samples. J. Pathol. Inform. 7 (2016)

    Google Scholar 

  66. J. Gallego-Posada, D.A. Montoya-Zapata, O.L. Quintero-Montoya, Detection and diagnosis of breast tumors using deep convolutional neural networks. Med. Phys. 43, 3705 (2016)

    Google Scholar 

  67. H. Chen, Q. Dou, X. Wang, J. Qin, P.A. Heng, Mitosis detection in breast cancer histology images via deep cascaded networks, in Thirtieth AAAI Conference on Artificial Intelligence (2016)

    Google Scholar 

  68. J. Arevalo, F.A. González, R. Ramos-Pollán, J.L. Oliveira, M.A. Lopez, Convolutional neural networks for mammography mass lesion classification, in 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) (IEEE, 2015), pp. 797–800

    Google Scholar 

  69. A. Akselrod-Ballin, L. Karlinsky, S. Alpert, S. Hasoul, R. Ben-Ari, E. Barkan, A region based convolutional network for tumor detection and classification in breast mammography, in Deep Learning and Data Labeling for Medical Applications (2016, Springer, Cham), pp. 197–205

    Google Scholar 

  70. R.K. Samala, H.P. Chan, L. Hadjiiski, M.A. Helvie, J. Wei, K. Cha, Mass detection in digital breast tomosynthesis: Deep convolutional neural network with transfer learning from mammography. Med. Phys. 43(12), 6654–6666 (2016)

    Google Scholar 

  71. Z. Jiao, X. Gao, Y. Wang, J. Li, A deep feature based framework for breast masses classification. Neurocomputing 12(197), 221–231 (2016)

    Google Scholar 

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

  73. A. Cruz-Roa, H. Gilmore, A. Basavanhally, M. Feldman, S. Ganesan, N.N. Shih, J. Tomaszewski, F.A. González, A. Madabhushi, Accurate and reproducible invasive breast cancer detection in whole-slide images: a deep learning approach for quantifying tumor extent. Sci. Rep. 18(7), 46450 (2017)

    Google Scholar 

  74. M.H. Yap, G. Pons, J. Martí, S. Ganau, M. Sentís, R. Zwiggelaar, A.K. Davison, R. Martí, Automated breast ultrasound lesions detection using convolutional neural networks. IEEE J. Biomed. Health Inform. 22(4), 1218–1226 (2017)

    Google Scholar 

  75. A. Das, U.R. Acharya, S.S. Panda, S. Sabut, Deep learning-based liver cancer detection using watershed transform and Gaussian mixture model techniques. Cogn. Syst. Res. 1(54), 165–175 (2019)

    Google Scholar 

  76. P. Devi, P. Dabas, Liver tumour detection using artificial neural networks for medical images. Int. J. Innov. Res. Sci. Technol. 2(3), 34–38 (2015)

    Google Scholar 

  77. W. Li, Automatic segmentation of liver tumour in CT images with deep convolutional neural networks. J. Comput. Commun. 3(11), 146 (2015)

    Google Scholar 

  78. D. Kumar, A. Wong, D.A. Clausi, Lung nodule classification using deep features in CT images, in 2015 12th Conference on Computer and Robot Vision (IEEE, 2015), pp. 133–138

    Google Scholar 

  79. W. Sun, B. Zheng, W. Qian, Computer aided lung cancer diagnosis with deep learning algorithms, in Medical Imaging 2016: Computer-Aided Diagnosis, vol. 9785 (International Society for Optics and Photonics, 2016), p. 97850Z

    Google Scholar 

  80. R. Gruetzemacher, A. Gupta, Using deep learning for pulmonary nodule detection & diagnosis, in Twenty-Second Americas Conference on Information Systems, San Diego (2016)

    Google Scholar 

  81. R. Golan, C. Jacob, J. Denzinger, Lung nodule detection in CT images using deep convolutional neural networks, in 2016 International Joint Conference on Neural Networks (IJCNN) (IEEE, 2016), pp. 243–250

    Google Scholar 

  82. K. Hirayama, J.K. Tan, H. Kim, Extraction of GGO candidate regions from the LIDC database using deep learning, in 2016 16th International Conference on Control, Automation and Systems (ICCAS) (IEEE, 2016), pp. 724–727

    Google Scholar 

  83. S. Bhatia, Y. Sinha, L. Goel, Lung cancer detection: a deep learning approach, in Soft Computing for Problem Solving (Springer, Singapore, 2019), pp. 699–705

    Google Scholar 

  84. A. Teramoto, H. Fujita, O. Yamamuro, T. Tamaki, Automated detection of pulmonary nodules in PET/CT images: ensemble false‐positive reduction using a convolutional neural network technique. Med. Phys. 43(6Part1), 2821–2827 (2016)

    Google Scholar 

  85. Y. Bar, I. Diamant, L. Wolf, S. Lieberman, E. Konen, H. Greenspan, Chest pathology detection using deep learning with non-medical training, in 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI) (IEEE, 2015), pp. 294–297

    Google Scholar 

  86. Y. Bar, I. Diamant, L. Wolf, H. Greenspan, Deep learning with non-medical training used for chest pathology identification, in Medical Imaging 2015: Computer-Aided Diagnosis, vol. 9414 (International Society for Optics and Photonics, 2015), p. 94140V

    Google Scholar 

  87. A.A. Cruz-Roa, J.E. Ovalle, A. Madabhushi, F.A. Osorio, A deep learning architecture for image representation, visual interpretability and automated basal-cell carcinoma cancer detection, in International Conference on Medical Image Computing and Computer-Assisted Intervention (Springer, Berlin, 2013), pp. 403–410

    Google Scholar 

  88. A. Masood, A. Al-Jumaily, K. Anam, Self-supervised learning model for skin cancer diagnosis, in 2015 7th International IEEE/EMBS Conference on Neural Engineering (NER) (IEEE, 2015), pp. 1012–1015

    Google Scholar 

  89. M.H. Jafari, N. Karimi, E. Nasr-Esfahani, S. Samavi, S.M. Soroushmehr, K. Ward, K. Najarian, Skin lesion segmentation in clinical images using deep learning, in 2016 23rd International Conference on Pattern Recognition (ICPR) (IEEE, 2016), pp. 337–342

    Google Scholar 

  90. P. Sabouri, H. Gholam Hosseini, Lesion border detection using deep learning, in 2016 IEEE Congress on Evolutionary Computation (CEC) (IEEE, 2016), pp. 1416–1421

    Google Scholar 

  91. H. Chen, H. Zhao, J. Shen, R. Zhou, Q. Zhou, Supervised machine learning model for high dimensional gene data in colon cancer detection, in 2015 IEEE International Congress on Big Data (IEEE, 2015), pp. 134–141

    Google Scholar 

  92. K. Sirinukunwattana, S.E. Raza, Y.W. Tsang, D.R. Snead, I.A. Cree, N.M. Rajpoot, Locality sensitive deep learning for detection and classification of nuclei in routine colon cancer histology images. IEEE Trans. Med. Imaging 35(5), 1196–1206 (2016)

    Google Scholar 

  93. C.L. Chen, A. Mahjoubfar, L.C. Tai, I.K. Blaby, A. Huang, K.R. Niazi, B. Jalali, Deep learning in label-free cell classification. Sci. Rep. 15(6), 21471 (2016)

    Google Scholar 

  94. X. Yuan, L. Xie, M. Abouelenien, A regularized ensemble framework of deep learning for cancer detection from multi-class, imbalanced training data. Pattern Recogn. 1(77), 160–172 (2018)

    Google Scholar 

  95. K.H. Cha, L. Hadjiiski, R.K. Samala, H.P. Chan, E.M. Caoili, R.H. Cohan, Urinary bladder segmentation in CT urography using deep-learning convolutional neural network and level sets. Med. Phys. 43(4), 1882–1896 (2016)

    Google Scholar 

  96. K.H. Cha, L.M. Hadjiiski, R.K. Samala, H.P. Chan, R.H. Cohan, E.M. Caoili, C. Paramagul, A. Alva, A.Z. Weizer, Bladder cancer segmentation in CT for treatment response assessment: application of deep-learning convolution neural network—a pilot study. Tomography 2(4), 421 (2016)

    Google Scholar 

  97. E. Shkolyar, X. Jia, T.C. Chang, D. Trivedi, K.E. Mach, M.Q. Meng, L. Xing, J.C. Liao, Augmented bladder tumor detection using deep learning. Eur. Urol. 76(6), 714–718 (2019)

    Google Scholar 

  98. T. Xu, H. Zhang, X. Huang, S. Zhang, D.N. Metaxas, Multimodal deep learning for cervical dysplasia diagnosis. in International Conference on Medical Image Computing and Computer-Assisted Intervention (Springer, Cham, 2016), pp. 115–123

    Google Scholar 

  99. S. Liu, H. Zheng, Y. Feng, W. Li, Prostate cancer diagnosis using deep learning with 3D multiparametric MRI, in Medical Imaging 2017: Computer-Aided Diagnosis, vol. 10134 ( International Society for Optics and Photonics, 2017), p. 1013428

    Google Scholar 

  100. Y.K. Tsehay, N.S. Lay, H.R. Roth, X. Wang, J.T. Kwak, B.I. Turkbey, P.A. Pinto, B.J. Wood, R.M. Summers, Convolutional neural network based deep-learning architecture for prostate cancer detection on multiparametric magnetic resonance images, in Medical Imaging 2017: Computer-Aided Diagnosis, vol. 10134 (International Society for Optics and Photonics, 2017), p. 1013405

    Google Scholar 

  101. A.R. Rajanna, R. Ptucha, S. Sinha, B. Chinni, V. Dogra, N.A. Rao, Prostate cancer detection using photoacoustic imaging and deep learning. Electron. Imaging 2016(15), 1–6 (2016)

    Google Scholar 

  102. M. Havaei, A. Davy, D. Warde-Farley, A. Biard, A. Courville, Y. Bengio, C. Pal, P.M. Jodoin, H. Larochelle, Brain tumor segmentation with deep neural networks. Med. Image Anal. 1(35), 18–31 (2017)

    Google Scholar 

  103. Z. Xiao, R. Huang, Y. Ding, T. Lan, R. Dong, Z. Qin, X. Zhang, W. Wang, A deep learning-based segmentation method for brain tumor in MR images, in 2016 IEEE 6th International Conference on Computational Advances in Bio and Medical Sciences (ICCABS) (IEEE, 2016), pp. 1–6

    Google Scholar 

  104. H. Dong, G. Yang, F. Liu, Y. Mo, Y. Guo, Automatic brain tumor detection and segmentation using u-net based fully convolutional networks, in Annual Conference on Medical Image Understanding and Analysis (Springer, Cham, 2017), pp. 506–517

    Google Scholar 

  105. M. Rezaei, K. Harmuth, W. Gierke, T. Kellermeier, M. Fischer, H. Yang, C. Meinel, A conditional adversarial network for semantic segmentation of brain tumor, in International MICCAI Brainlesion Workshop (Springer, Cham, 2017), pp. 241–252

    Google Scholar 

  106. H. Mohsen, E.S. El-Dahshan, E.S. El-Horbaty, A.B. Salem, Classification using deep learning neural networks for brain tumors. Future Comput. Inform. J. 3(1), 68–71 (2018)

    Google Scholar 

  107. X. Zhao, Y. Wu, G. Song, Z. Li, Y. Zhang, Y. Fan, A deep learning model integrating FCNNs and CRFs for brain tumor segmentation. Med. Image Anal. 1(43), 98–111 (2018)

    Google Scholar 

  108. K. Munir, H. Elahi, A. Ayub, F. Frezza, A. Rizzi, Cancer diagnosis using deep learning: a bibliographic review. Cancers 11(9), 1235 (2019)

    Google Scholar 

  109. M.Z. Alom, T.M. Taha, C. Yakopcic, S. Westberg, P. Sidike, M.S. Nasrin, M. Hasan, B.C. Van Essen, A.A. Awwal, V.K. Asari, A state-of-the-art survey on deep learning theory and architectures. Electronics 8(3), 292 (2019)

    Google Scholar 

Download references

Acknowledgements

The authors would like to acknowledge the support provided by Nanyang Technologıcal Unıversıty under NTU Ref: RCA-17/334 for providing the medical images and supporting us in the preparation of the manuscript. Parasuraman Padmanabhan and Balazs Gulyas also acknowledge the support from Lee Kong Chian School of Medicine and Data Science and AI Research (DSAIR) centre of NTU (Project Number ADH-11/2017-DSAIR) and the support from the Cognitive NeuroImaging Centre (CONIC) at NTU.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to S. N. Kumar .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2021 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

About this chapter

Verify currency and authenticity via CrossMark

Cite this chapter

Kumar, S.N., Lenin Fred, A., Padmanabhan, P., Gulyas, B., Ajay Kumar, H., Jonisha Miriam, L.R. (2021). Deep Learning Algorithms in Medical Image Processing for Cancer Diagnosis: Overview, Challenges and Future. In: Kose, U., Alzubi, J. (eds) Deep Learning for Cancer Diagnosis. Studies in Computational Intelligence, vol 908. Springer, Singapore. https://doi.org/10.1007/978-981-15-6321-8_3

Download citation