Ischemic brain stroke detection from MRI image using logistic regression classifier

Md Shabuj Hossain, Subir Saha, Liton Chandra Paul, Rezaul Azim, Abdulla Al Suman

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contributionpeer-review

Abstract

In this paper an efficient model for ischemic brain stroke detection from magnetic resonance imaging (MRI) using machine learning approach namely logistic regression classifier is proposed. The MRI images are pre-processed to reduce noise and converted into gray images. Then the stroke portions of the MRI gray images are segmented by using hue, saturation, and value (HSV) color threshold and the segmented stroke images are converted into binary images to reduce computational complexity. The stroke features namely mean hue, standard deviation, mean variance and area of affected lesion i.e. stroke portion have been extracted. Finally, logistic regression classifier is used to identify the classes of test image. The proposed model shows an accuracy of 96%, sensitivity of 92.3% and specificity of 100% for testing datasets.

Original languageEnglish
Title of host publicationICREST 2021
Subtitle of host publication2nd International Conference on Robotics, Electrical and Signal Processing Techniques
Place of PublicationBangladesh
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages763-767
Number of pages5
ISBN (Electronic)9780738130408, 9781665415767
ISBN (Print)9781665415774
DOIs
Publication statusPublished - 5 Jan 2021
Externally publishedYes
Event2nd International Conference on Robotics, Electrical and Signal Processing Techniques, ICREST 2021 - Virtual Event, Bangladesh
Duration: 5 Jan 20217 Jan 2021

Conference

Conference2nd International Conference on Robotics, Electrical and Signal Processing Techniques, ICREST 2021
CountryBangladesh
Period5/01/217/01/21

Keywords

  • Brain stroke detection
  • HSV color space
  • Logistic regression classifier
  • Machine learning
  • MRI

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