A review on computer aided diagnosis of acute brain stroke

Mahesh Anil Inamdar, Udupi Raghavendra, Anjan Gudigar, Yashas Chakole, Ajay Hegde, Girish R. Menon, Prabal Barua, Elizabeth Emma Palmer, Kang Hao Cheong, Wai Yee Chan, Edward J. Ciaccio, U. Rajendra Acharya

Research output: Contribution to journalReview articlepeer-review

3 Citations (Scopus)

Abstract

Amongst the most common causes of death globally, stroke is one of top three affecting over 100 million people worldwide annually. There are two classes of stroke, namely ischemic stroke (due to impairment of blood supply, accounting for ~70% of all strokes) and hemorrhagic stroke (due to bleeding), both of which can result, if untreated, in permanently damaged brain tissue. The discovery that the affected brain tissue (i.e., ‘ischemic penumbra’) can be salvaged from permanent damage and the bourgeoning growth in computer aided diagnosis has led to major advances in stroke management. Abiding to the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) guidelines, we have surveyed a total of 177 research papers published between 2010 and 2021 to highlight the current status and challenges faced by computer aided diagnosis (CAD), machine learning (ML) and deep learning (DL) based techniques for CT and MRI as prime modalities for stroke detection and lesion region segmentation. This work concludes by showcasing the current requirement of this domain, the preferred modality, and prospective research areas.

Original languageEnglish
Article number8507
JournalSensors
Volume21
Issue number24
DOIs
Publication statusPublished - 01-12-2021

All Science Journal Classification (ASJC) codes

  • Analytical Chemistry
  • Information Systems
  • Atomic and Molecular Physics, and Optics
  • Biochemistry
  • Instrumentation
  • Electrical and Electronic Engineering

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