Discrimination Between Stroke and Brain Tumour in CT Images Based on the Texture Analysis

Kobus, M; Sobczak, Karolina; Jangas, Mariusz; Świątek, Adrian; Strzelecki, Michał

doi:10.1007/978-3-031-09135-3_15

Cited by 2 publications

(1 citation statement)

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“…Their study included two phases: classification and segmentation of brain stroke CT images. Currently, Deep Learning algorithms (DL), ML algorithms, and created hybrid algorithms with DL–ML approaches are utilized in many studies for detecting brain stroke [ 8 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 ].…”

Section: Literature Reviewmentioning

confidence: 99%

A Deep Learning Approach for Detecting Stroke from Brain CT Images Using OzNet

Özaltın

Coskun²,

Yeniay

et al. 2022

Bioengineering

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A brain stroke is a life-threatening medical disorder caused by the inadequate blood supply to the brain. After the stroke, the damaged area of the brain will not operate normally. As a result, early detection is crucial for more effective therapy. Computed tomography (CT) images supply a rapid diagnosis of brain stroke. However, while doctors are analyzing each brain CT image, time is running fast. This circumstance may lead to result in a delay in treatment and making errors. Therefore, we targeted the utilization of an efficient artificial intelligence algorithm in stroke detection. In this paper, we designed hybrid algorithms that include a new convolution neural networks (CNN) architecture called OzNet and various machine learning algorithms for binary classification of real brain stroke CT images. When we classified the dataset with OzNet, we acquired successful performance. However, for this target, we combined it with a minimum Redundancy Maximum Relevance (mRMR) method and Decision Tree (DT), k-Nearest Neighbors (kNN), Linear Discriminant Analysis (LDA), Naïve Bayes (NB), and Support Vector Machines (SVM). In addition, 4096 significant features were obtained from the fully connected layer of OzNet, and we reduced the dimension of features from 4096 to 250 using the mRMR method. Finally, we utilized these machine learning algorithms to classify important features. As a result, OzNet-mRMR-NB was an excellent hybrid algorithm and achieved an accuracy of 98.42% and AUC of 0.99 to detect stroke from brain CT images.

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