A Novel Weighted Average 2D-CNN Ensemble for Intracranial Hemorrhage CT Image Classification

A Novel Weighted Average 2D-CNN Ensemble for Intracranial Hemorrhage CT Image Classification
  IJETT-book-cover           
  
© 2024 by IJETT Journal
Volume-72 Issue-7
Year of Publication : 2024
Author : Santwana Gudadhe, Anuradha Thakare
DOI : 10.14445/22315381/IJETT-V72I7P126

How to Cite?

Santwana Gudadhe, Anuradha Thakare, "A Novel Weighted Average 2D-CNN Ensemble for Intracranial Hemorrhage CT Image Classification," International Journal of Engineering Trends and Technology, vol. 72, no. 7, pp. 247-255, 2024. Crossref, https://doi.org/10.14445/22315381/IJETT-V72I7P126

Abstract
Rapid and precise diagnosis and treatment of brain hemorrhage is of the highest importance because of the serious danger it presents to human life. Depending on where and how the bleeding is occurring, several distinct kinds of brain hemorrhage can be distinguished. Five subtypes of hemorrhage are covered in the primary division: subdural, epidural, intraventricular, intraparenchymal, and subarachnoid. Intracranial hemorrhage Computed Tomography (CT) image classification using the proposed weighted average 2D-Convolution neural network model has been proposed for intracranial hemorrhage subtype CT image classification. Intracranial hemorrhage subtypes, Intracranial Hemorrhage (ICH), Subdural Hemorrhage (SAH), Epidural Hemorrhage (EDH), and normal subtypes are outlined in this article. Three separate neural network models using two-dimensional convolution were evaluated for the purpose of bleeding subtype classification. Finally, a new ensemble model for a weighted average 2D convolution neural network has been designed. The suggested ensemble model can distinguish between 4 types of hemorrhage. Applying the proposed model to the test dataset reveals a maximum accuracy of 95.86%. In terms of classification, the suggested model can achieve a respectable degree of precision.

Keywords
Intracranial hemorrhage, Convolution neural network, Weighted average 2D-convolution neural network ensemble model, Classification.

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