CT Image Classification of Human Brain using Deep Learning

Authors

  • Archana Kalidindi Gokaraju Rangaraju Institute of Engineering and Technology https://orcid.org/0000-0003-1297-5828
  • Prasanna Lakshmi Kompalli Gokaraju Rangaraju Institute of Engineering and Technology https://orcid.org/0000-0002-8496-774X
  • Sairam Bandi Gokaraju Rangaraju Institute of Engineering and Technology
  • Sri Raagh Rao Anugu Gokaraju Rangaraju Institute of Engineering and Technology

DOI:

https://doi.org/10.3991/ijoe.v17i01.18565

Keywords:

Computed Tomography, Deep Learning, CNN, Hemorrhage

Abstract


Computed Tomography (CT) images are cross-sectional images of any specific area of a human body which allows doctors to see inside of a patient. CT scan is almost always the first imaging modality used to assess patients with suspected hemorrhage. A CT scan provides image reports in the form of grey shades. It is sometimes difficult to distinguish between two areas because the shades of grey in a CT image are occasionally similar. CT scan (Particularly “Non-Contrast Head CT Scan”) is the current guideline for primary imaging of patients with any head injuries or brain stroke like symptoms. To obtain any findings from the CT image, Radiologists or other doctors need to examine the images. Deep-learning is an important tool used in radiology and medical imaging which provides a better understanding of the image with more efficiency and quicker exam time. The main idea of this project is developing a model using classification algorithms which can be used to classify or detect hemorrhage in a CT image. The dataset consists of both normal CTs and CTs with hemorrhage. Deep learning is used to develop a model that can detect whether a CT image shows a hemorrhage or not.

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Published

2021-01-19

How to Cite

Kalidindi, A., Kompalli, P. L., Bandi, S., & Anugu, S. R. R. (2021). CT Image Classification of Human Brain using Deep Learning. International Journal of Online and Biomedical Engineering (iJOE), 17(01), pp. 51–62. https://doi.org/10.3991/ijoe.v17i01.18565

Issue

Vol. 17 No. 01 (2021)

Section

Papers

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