"Recognition of brain stroke shape using multiscale morphological image" by S. Venkata Lakshmi, M. Anline Rejula et al.
 

Recognition of brain stroke shape using multiscale morphological image processing

Document Type

Article

Publication Date

1-1-2021

Abstract

Brain haemorrhage is a form of stroke caused by the inflation in the cerebral artery ensuing confined bleeding in the inherent tissues. The MRI images (T1, T2 and FLAIR (fluid-attenuated inversion recovery)) are extracted from Siemen 3T scanner in which the ordinary and haemorrhage-affected strokes are effectively emphasized in this work. The existing such as CNN (convolutional neural network), residual CNN and MCDNN (mobile-cloud deep neural network) are compared from the perspective of PSNR (peak signal-to-noise ratio), SSIM (structural similarity index measure) and MSE (mean squared error), respectively. The PSNR value of this proposed MMNN (multiple nonotonic neural network) is increased by 0.23%, 0.087% and 0.613% compared to CNN, Residual CNN and MCDNN techniques, respectively. The SSIM value is increased by 0.57%, 0.322% and 0.027% compared to CNN, Residual CNN and MCDNN. MSE value is decreased by 8.93%, 2.1457% and 0.316% compared to CNN, Residual CNN and MCDNN, respectively.

Publication Title

Imaging Science Journal

First Page Number

28

Last Page Number

37

DOI

10.1080/13682199.2022.2146877

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