Citation: Sengupta, J.; Alzbutas, R.
Intracranial Hemorrhages
Segmentation and Features Selection
Applying Cuckoo Search Algorithm
with Gated Recurrent Unit. Appl. Sci.
2022, 12, 10851. https://doi.org/
10.3390/app122110851
Academic Editor: Silvia Liberata
Ullo
Received: 7 August 2022
Accepted: 21 September 2022
Published: 26 October 2022
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Article
Intracranial Hemorrhages Segmentation and Features Selection
Applying Cuckoo Search Algorithm with Gated Recurrent Unit
Jewel Sengupta
1,
* and Robertas Alzbutas
2
1
Department of Applied Mathematics, Kaunas University of Technology, K. Donelaiˇcio g. 73,
44249 Kaunas, Lithuania
2
Department of Applied Mathematics, Faculty of Mathematics and Natural Sciences, Kaunas University of
Technology, K. Donelaiˇcio g. 73, 44249 Kaunas, Lithuania
* Correspondence: jewel.sengupta@ktu.edu
Abstract:
Generally, traumatic and aneurysmal brain injuries cause intracranial hemorrhages, which
is a severe disease that results in death, if it is not treated and diagnosed properly at the early
stage. Compared to other imaging techniques, Computed Tomography (CT) images are extensively
utilized by clinicians for locating and identifying intracranial hemorrhage regions. However, it is a
time-consuming and complex task, which majorly depends on professional clinicians. To highlight
this problem, a novel model is developed for the automatic detection of intracranial hemorrhages.
After collecting the 3D CT scans from the Radiological Society of North America (RSNA) 2019
brain CT hemorrhage database, the image segmentation is carried out using Fuzzy C Means (FCM)
clustering algorithm. Then, the hybrid feature extraction is accomplished on the segmented regions
utilizing the Histogram of Oriented Gradients (HoG), Local Ternary Pattern (LTP), and Local Binary
Pattern (LBP) to extract discriminative features. Furthermore, the Cuckoo Search Optimization (CSO)
algorithm and the Optimized Gated Recurrent Unit (OGRU) classifier are integrated for feature
selection and sub-type classification of intracranial hemorrhages. In the resulting segment, the
proposed ORGU-CSO model obtained 99.36% of classification accuracy, which is higher related to
other considered classifiers.
Keywords:
cuckoo search optimizer; Fuzzy C Mean; gated recurrent unit; hybrid feature extraction;
intracranial hemorrhage
1. Introduction
The intracranial hemorrhage disease is caused in the brain due to the leakage in the
blood vessels that leads to inactive body functions such as memory loss, speech, and
eyesight [
1
]. The major risk factors in intracranial hemorrhages are infected blood vessel
walls, and leakage in the vein [
2
]. Compared to other imaging modalities, CT imaging is
the preferred modality in intracranial hemorrhage detection because of its limited cost,
high sensitivity, rapidity, and wide availability [
3
]. The intracranial hemorrhage lesions are
brightly characterized in the CT imaging modality. The manual detection of intracranial
hemorrhage lesions from the CT scan remains challenging because of artifacts in CT scans,
uneven boundaries, noise, and overlapping pixel intensities [
4
,
5
]. Hence, the manual
demarcation is subject to the intra-observer and inter-observer, and it is heavily dependent
on the physician’s expertise [6].
Additionally, the complexities and irregularities associated with varied sizes and
shapes of intracranial hemorrhage lesions make the segmentation and classification process
more strenuous and difficult [
7
,
8
]. Intracranial hemorrhage detection becomes a daunting
and laborious task, especially in large clinical settings, which introduce delay and inad-
vertent error. Therefore, the development of automated models supports physicians in
making efficient, reliable, and rapid intracranial hemorrhage lesions detection from 3D
Appl. Sci. 2022, 12, 10851. https://doi.org/10.3390/app122110851 https://www.mdpi.com/journal/applsci
