Skip to main content
SpringerLink
Log in

Case Study: Deep Convolutional Networks in Healthcare

  • Chapter
  • First Online:
Development and Analysis of Deep Learning Architectures

Part of the book series: Studies in Computational Intelligence ((SCI,volume 867))

Abstract

Technological improvements lead big data producing, processing and storing systems. These systems must contain extraordinary capabilities to overcome complexity of the big data. Therefore, the methodologies utilized for data analysis have been evolved due to the increase in importance of extracting information from big data. Healthcare systems are important systems dealing with big data analysis. Deep learning is the most applied data analysis method. It becomes one of the most popular and up-to-date artificial neural network types with deep representation ability. Another powerful ability of deep learning is providing feature learning through convolutional neural networks. Deep learning has wide implementation areas in medical applications from diagnosis to treatment. Various deep learning methods are applied to the biomedical problems. In many applications, deep learning solutions are modified in accordance with the requirements of the problems. Through this chapter the most popular and up-to-date deep learning solutions to biomedical problems are discussed. Studies are analyzed according to problem characteristic, importance of solution, requirements and deep learning approaches to solve them. Since the deep learning systems have very effective image and pattern recognition ability, biomedical imaging becomes one of the most suitable application areas. During the first diagnosis and continuous tracking phase of the patients, deep learning systems offer very effective aids to the medicine. Although organ, disease or data type classifications are possible for biomedical application categorization, organ and disease combination are taken into consideration in the chapter.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 139.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 179.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 179.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Miotto, R., Wang, F., Wang, S., Jiang, X., Dudley, J.T.: Deep learning for healthcare: review, opportunities and challenges. Brief. Bioinf. 19(6), 1236–1246

    Article  Google Scholar 

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

    Article  Google Scholar 

  3. Brosch, T., Tam, R.: Manifold learning of brain MRIs by deep learning. Med. Image Comput. Comput. Assist. Interv. 16(2), 633–640 (2013)

    Google Scholar 

  4. Sarraf, S., Tofighi, G.: Deep learning-based pipeline to recognize Alzheimer’s disease using fMRI data. In: FTC 2016—Future Technologies Conference 2016, 6–7 December 2016, San Francisco, United States, pp. 816–820 (2016)

    Google Scholar 

  5. Li, R., Zhang, W., Suk, H., Wang, L., Li, J., Shen, D., Ji, S.: Deep learning based imaging data completion for improved brain disease diagnosis. Med. Image Comput. Comput. Assist. Interv. 17(3), 305–312 (2014)

    Google Scholar 

  6. Islam, J., Zhang, Y.: Brain MRI analysis for Alzheimer’s disease diagnosis using an ensemble system of deep convolutional neural networks. Brain Inf. 5, 2 (2018)

    Article  Google Scholar 

  7. Nie, D., Zhang, H., Adeli, E., Liu, L., Shen, D.: 3D deep learning for multi-modal imaging-guided survival time prediction of brain tumor patients. Med. Image Comput. Comput. Assist. Interv. 9901, 212–220 (2016)

    Google Scholar 

  8. Brosch, T., Yoo, Y., Li, D.K.B., Traboulsee, A., Tam, R.: Modeling the variability in brain morphology and lesion distribution in multiple sclerosis by deep learning. Med. Image Comput. Comput. Assist. Interv. 17(2), 462–469 (2014)

    Google Scholar 

  9. Hammerla, N.Y., et al.: PD disease state assessment in naturalistic environments using deep learning. In: AAAI’15 Proceedings of the Twenty-Ninth AAAI Conference on Artificial Intelligence, Austin, Texas, pp. 1742–1748 (2015)

    Google Scholar 

  10. Kallenberg, M., et al.: Unsupervised deep learning applied to breast density segmentation and mammographic risk scoring. IEEE Trans. Med. Imaging 35(5), 1322–1331 (2016)

    Article  Google Scholar 

  11. Wang, J., Yang, X., Cai, H., Tan, W., Jin, C., Li, L.: Discrimination of breast cancer with microcalcifications on mammography by deep learning. Sci. Rep. 6, 27327 (2016)

    Google Scholar 

  12. Kooi, T., Litjens, G., Ginneken, B., Gubern-Merida, A., Sanchez, I.C., Mann, R., Heeten, A., Karssemeijer, N.: Large scale deep learning for computer aided detection of mammographic lesions. Med. Image Anal. 35, 303–312 (2017)

    Article  Google Scholar 

  13. Dhungel, N., Carneiro, G., Bradley, A.P.: A deep learning approach for the analysis of masses in mammograms with minimal user intervention. Med. Image Anal. 37, 114–128 (2017)

    Article  Google Scholar 

  14. Litjens, G., et al.: Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis. Sci. Rep. 6, 26286 (2016)

    Article  Google Scholar 

  15. Lunscher, N., Chen, M.L., Jiang, N., Zelek, J.: Automated screening for diabetic retinopathy using compact deep networks. J. Comput. Vision Imaging Syst. 3(1) (2017)

    Google Scholar 

  16. Ting, D.S.W., et al.: Development and validation of a deep learning system for diabetic retinopathy and related eye diseases using retinal images from multiethnic populations with diabetes. JAMA 318(22), 2211–2223 (2017)

    Article  Google Scholar 

  17. Tao, X., Zhang, H., Huang, X., Zhang, S., Metaxas, D.N.: Medical image computing and computer-assisted intervention—MICCAI 2016. Lecture Notes in Computer Science, vol. 9901. Springer, Cham

    Google Scholar 

  18. Yan, Z., et al.: Multi-instance deep learning: discover discriminative local anatomies for bodypart recognition. IEEE Trans. Med. Imaging 35(5), 1332–1343 (2016)

    Article  Google Scholar 

  19. Shimizu, R., Yanagawa, S., Monde, Y., Yamagishi, H., Hamada, M., Shimizu, T., Kuroda, T.: Deep learning application trial to lung cancer diagnosis for medical sensor systems. In: 2016 International SoC Design Conference (ISOCC) (2016)

    Google Scholar 

  20. Vieira, S., Pinaya, W.H.L., Mechelli, A.: Using deep learning to investigate the neuroimaging correlates of psychiatric and neurological disorders: methods and applications. Neurosci. Biobehav. Rev. 74, 58–75 (2017)

    Article  Google Scholar 

  21. Avendi, M.R., Kheradvar, A., Jafarkhani, H.: A combined deep-learning and deformable-model approach to fully automatic segmentation of the left ventricle in cardiac MRI. Med. Image Anal. 30, 108–119 (2016)

    Article  Google Scholar 

  22. Cruz-Roa, A., et al.: Accurate and reproducible invasive breast cancer detection in whole slide images: a deep learning approach for quantifying tumor extent. Sci. Rep. 7, 46450 (2017)

    Google Scholar 

  23. Levy, D., Jain, A.: Breast mass classification from mammograms using deep convolutional neural networks (2016). arXiv:1612.00542

  24. Liu, S., Liu, S., Cai, W., Pujoi, S., Kikinis, R., Feng, D.: Early diagnosis of Alzheimer’s disease with deep learning. In: 2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI), Beijing, pp. 1015–1018 (2014)

    Google Scholar 

  25. Rahhal, M.M., Bazi, Y., AlHichri, H., Alajlan, N., Melgani, F., Yager, R.R.: Deep learning approach for active classification of electrocardiogram signals. Inf. Sci. 345, 340–354 (2016)

    Article  Google Scholar 

  26. Chen, M., Shi, X., Zhang, Y., Wu, D., Guizani, M.: Deep feature learning for medical image analysis with convolutional autoencoder neural network. IEEE Trans. Big Data (2017)

    Google Scholar 

  27. Gao, L., Lin, S., Wong, T.Y.: Automatic feature learning to grade nuclear cataracts based on deep learning. IEEE Trans. Biomed. Eng. 62(11), 2693–2701 (2015)

    Article  Google Scholar 

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

    Article  Google Scholar 

  29. Chen, H., Yu, L., Dou, Q., Shi, L., Mok, V.C.T., Heng, P.A.: Automatic detection of cerebral microbleeds via deep learning based 3D feature representation. In: 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI), New York, NY, pp. 764–767 (2015)

    Google Scholar 

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

  31. Han, Z., Wei, B., Zheng, Y., Yin, Y., Li, K., Li, S.: Breast cancer multi-classification from histopathological images with structured deep learning model. Sci. Rep. 7, 4172 (2017)

    Google Scholar 

  32. Akkus, Z., Galimzianova, A., Hoogi, A., Rubin, D.L.: Deep learning for brain MRI segmentation: state of the art and future directions. J. Digit. Imaging 30, 449–459 (2017)

    Article  Google Scholar 

  33. Milletari, F., et al.: Hough-CNN: deep learning for segmentation of deep brain regions in MRI and ultrasound. Comput. Vis. Image Underst. 164, 92–102 (2017)

    Article  Google Scholar 

  34. Lee, C.S., Tyring, A.J., Deruyter, N.P., Wu, Y., Rokem, A., Lee, A.Y.: Deep-learning based, automated segmentation of macular edema in optical coherence tomography. Biomed. Opt. Express 8(7), 3440 (2017)

    Article  Google Scholar 

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

    Article  Google Scholar 

  36. Dhungel, N., Carneiro, G., Bradley, A.P.: Deep learning and structured prediction for the segmentation of mass in mammograms, medical image computing and computer-assisted intervention—MICCAI 2015. Lecture Notes in Computer Science, vol. 9349. Springer, Cham (2015)

    Google Scholar 

  37. Dhungel, N., Carneiro, G., Bradley A.P.: Automated mass detection in mammograms using cascaded deep learning and random forests. In: 2015 International Conference on Digital Image Computing: Techniques and Applications (DICTA), 23–25 Nov 2015, Adelaide, SA, pp. 1–8 (2015)

    Google Scholar 

  38. Carneiro, G., Nascimento, J., Bradley A.P.: Unregistered multiview mammogram analysis with pre-trained deep learning models, medical image computing and computer-assisted intervention—MICCAI 2015. Lecture Notes in Computer Science, vol. 9351. Springer, Cham (2015)

    Google Scholar 

  39. Dalmis, M.U., Litjens, G., Holland, K., Setio, A., Mann, R., Karssemeijer, N., Gubern-Merida, A.: Using deep learning to segment breast and fibroglandular tissue in MRI volumes. Med. Phys. 44, 533–546 (2017)

    Article  Google Scholar 

  40. Isin, A., Direkoglu, C., Sah, M.: Review of MRI-based brain tumor image segmentation using deep learning methods. In: 12th International Conference on Application of Fuzzy Systems and Soft Computing, ICAFS 2016, 29–30 August 2016, Vienna, Austria, pp. 317–324 (2016)

    Google Scholar 

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

    Article  Google Scholar 

  42. Suk, H., Lee, S.W., Shen, D.: The Alzheimer’s disease neuroimaging initiative, hierarchical feature representation and multimodal fusion with deep learning for AD/MCI diagnosis. NeuroImage 101, 569–582 (2014)

    Article  Google Scholar 

  43. Lai, M.: Deep learning for medical image segmentation (2015). arXiv:1505.02000v1

  44. Gulshan, V., et al.: Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA 316(22), 2402–2410 (2016)

    Article  Google Scholar 

  45. Abràmoff, M.D., Lou, Y., Erginay, A., Clarida, W., Amelon, R., Folk, J.C., Niemeijer, M.: Improved automated detection of diabetic retinopathy on a publicly available dataset through integration of deep learning. Invest. Ophthalmol. Vis. Sci. 57(13), 5200–5206 (2016)

    Article  Google Scholar 

  46. Fu, H., Xu, Y., Wong, D.W.K., Liu, J.: DeepVessel: retinal vessel segmentation via deep learning and conditional random field. In: International Conference on Medical Image Computing and Computer-Assisted Intervention MICCAI 2016: Medical Image Computing and Computer-Assisted Intervention—MICCAI 2016, pp 132–139 (2016)

    Chapter  Google Scholar 

  47. Liskowski, P., Krawiec, K.: Segmenting retinal blood vessels with deep neural networks. IEEE Trans. Med. Imaging 35(11), 2369–2380 (2016)

    Article  Google Scholar 

  48. Emad, O., Yassine, I.A., Fahmy, A.S.: Automatic localization of the left ventricle in cardiac MRI images using deep learning. In: 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Milan, pp. 683–686 (2015)

    Google Scholar 

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

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Faculty of Engineering, Biomedical Engineering Department, Cukurova University, Adana, Turkey

    Mutlu Avci

  2. Computer Engineering Department, Iskenderun Technical University, Hatay, Turkey

    Mehmet Sarıgül

  3. Faculty of Engineering, Computer Engineering Department, Cukurova University, Adana, Turkey

    Buse Melis Ozyildirim

Authors
  1. Mutlu Avci
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mehmet Sarıgül
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Buse Melis Ozyildirim
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Buse Melis Ozyildirim .

Editor information

Editors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Alberta, Edmonton, AB, Canada

    Witold Pedrycz

  2. Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei, Taiwan

    Shyi-Ming Chen

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Avci, M., Sarıgül, M., Ozyildirim, B.M. (2020). Case Study: Deep Convolutional Networks in Healthcare. In: Pedrycz, W., Chen, SM. (eds) Development and Analysis of Deep Learning Architectures. Studies in Computational Intelligence, vol 867. Springer, Cham. https://doi.org/10.1007/978-3-030-31764-5_3

Download citation

Publish with us

Policies and ethics

Search

Navigation