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Convolution Neural Networks: A Case Study on Brain Tumor Segmentation in Medical Care

  • Jayanthi PrisillaEmail author
  • V. Murali Krishna Iyyanki
Conference paper
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 30)

Abstract

Image segmentation is dividing of medical imaging into parts and extracting the regions of interest. The study involves the images of brain tumors where the tumor part is segmented from the image and analyzed accurately and efficiently. Convolution Neural Network (CNN) has made a tremendous progress in the field of the Medical and Information Technology. With CNN model, one may not be able to reorganize higher risk patients to get immediate aid they require but also communicate through the network to the clinicians, surgeons, eventually improving the standard of patient care in the medical system.

Keywords

Convolutional neural network Gray-level Pixel Segmentation 

Notes

Conflicts of Interest

Prisilla Jayanthi is the principal investigator of the CNN study and involved in script making. Dr. I. V. Murali Krishna is a keen guide in directing the research work and giving the novel ideas. The X-ray reports were collected from the Gandhi Hospital, Secunderabad, India and thanks for their support.

References

  1. 1.
    Yin Z et al (2015) A clinical decision support system for the diagnosis of probable migraine and probable tension-type headache based on case-based reasoning. J Headache Pain 16:29CrossRefGoogle Scholar
  2. 2.
    Moses AJ et al (2006) Computer-aided diagnoses of chronic head pain: explanation, study data, implications, and challenges. J Craniomandibular PractGoogle Scholar
  3. 3.
    Grill E et al (2016) Developing and implementing diagnostic prediction models for vestibular diseases in primary care. In: Exploring complexity in health: an interdisciplinary systems approachGoogle Scholar
  4. 4.
    Mirarchi D et al (2016) Applying mining techniques to analyze vestibular data. Elsevier Procedia Comput Sci 98(2016):467–472CrossRefGoogle Scholar
  5. 5.
    Nishara Banu MA, Gomathy B (2013) Disease predicting system using data mining techniques. Int J Tech Res Appl. e-ISSN: 2320-8163Google Scholar
  6. 6.
    Halicek M et al (2017) Deep convolutional neural networks for classifying head and neck cancer using hyperspectral imaging. J Biomed Opt 22(6):060503CrossRefGoogle Scholar
  7. 7.
    Prisilla J et al (2017) Deep learning in oncology—a case study on brain tumor. Int J Cancer Res Ther. ISSN: 2476-237Google Scholar
  8. 8.
    Prisilla J (2016) The butterfly network in medical care using clouds services. ICTBIG IEEE.  https://doi.org/10.1109/ictbig.2016.7892655. Electronic ISBN: 978-1-5090-5515-9
  9. 9.
    Chen K, Seuret M (2017) Convolutional neural networks for page segmentation of historical document imagesGoogle Scholar
  10. 10.
    Gonzalez RC, Woods RE, Digital image processing. Pearson Education AsiaGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Jayanthi Prisilla
    • 1
    Email author
  • V. Murali Krishna Iyyanki
    • 2
  1. 1.HyderabadIndia
  2. 2.Defence Research and Development OrganizationHyderabadIndia

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