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Deep Learning for Medical Image Processing: Overview, Challenges and the Future

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Classification in BioApps

Part of the book series: Lecture Notes in Computational Vision and Biomechanics ((LNCVB,volume 26))

Abstract

The health care sector is totally different from any other industry. It is a high priority sector and consumers expect the highest level of care and services regardless of cost. The health care sector has not achieved society’s expectations, even though the sector consumes a huge percentage of national budgets. Mostly, the interpretations of medical data are analyzed by medical experts. In terms of a medical expert interpreting images, this is quite limited due to its subjectivity and the complexity of the images; extensive variations exist between experts and fatigue sets in due to their heavy workload. Following the success of deep learning in other real-world applications, it is seen as also providing exciting and accurate solutions for medical imaging, and is seen as a key method for future applications in the health care sector. In this chapter, we discuss state-of-the-art deep learning architecture and its optimization when used for medical image segmentation and classification. The chapter closes with a discussion of the challenges of deep learning methods with regard to medical imaging and open research issue.

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Notes

  1. 1.

    https://github.com/BVLC/caffe/tree/master/models/bvlcalexnet

  2. 2.

    http://deeplearning.net/tutorial/lenet.html

  3. 3.

    https://github.com/ShaoqingRen/fasterrcnn

  4. 4.

    https://github.com/BVLC/caffe/tree/master/models/bvlcgooglenet

  5. 5.

    https://github.com/gcr/torch-residual-networks

  6. 6.

    http://www.robots.ox.ac.uk/vgg/research/verydeep/

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Author information

Authors and Affiliations

  1. King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia

    Muhammad Imran Razzak

  2. GGPGC1, Abbottabad, KPK, Pakistan

    Saeeda Naz

  3. Woman Medical College, Abbottabad, KPK, Pakistan

    Ahmad Zaib

Authors
  1. Muhammad Imran Razzak
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  2. Saeeda Naz
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  3. Ahmad Zaib
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Corresponding author

Correspondence to Muhammad Imran Razzak .

Editor information

Editors and Affiliations

  1. Department of Information Technology, Techno India College of Technology, Kolkata, India

    Nilanjan Dey

  2. Faculty of Engineering, Tanta University, Tanta, Egypt

    Amira S. Ashour

  3. K.S. Institute of Technology, Bangalore, Karnataka, India

    Surekha Borra

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Razzak, M.I., Naz, S., Zaib, A. (2018). Deep Learning for Medical Image Processing: Overview, Challenges and the Future. In: Dey, N., Ashour, A., Borra, S. (eds) Classification in BioApps. Lecture Notes in Computational Vision and Biomechanics, vol 26. Springer, Cham. https://doi.org/10.1007/978-3-319-65981-7_12

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  • DOI: https://doi.org/10.1007/978-3-319-65981-7_12

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