An Ensemble of Deep Learning Enabled Brain Stroke Classification Model in Magnetic Resonance Images

Abstract

Brain stroke is a major cause of global death and it necessitates earlier identification process to reduce the mortality rate. Magnetic resonance imaging (MRI) techniques is a commonly available imaging modality used to diagnose brain stroke. Presently, machine learning (ML) and deep learning (DL) models can be extremely utilized for disease detection and classification processes. Amongst the available approaches, the convolutional neural network (CNN) models have been widely used for computer vision and image processing issues such as ImageNet, facial detection, and digit classification. In this article, a novel computer aided diagnosis (CAD) based brain stroke detection and classification (CAD-BSDC) model has been developed for MRI images. The proposed CAD-BSDC technique aims in classifying the provided MR brain image as normal or abnormal. The CAD-BSDC technique involves different subprocesses such as preprocessing, feature extraction, and classification. Firstly, the input image undergoes preprocessing using adaptive thresholding (AT) technique for improving the image quality. Followed by, an ensemble of feature extractors such as MobileNet, CapsuleNet, and EfficientNet models are used. Besides, the hyperparameter tuning of the deep learning models takes place using the improved dragonfly optimization (IDFO) algorithm. Moreover, satin bowerbird optimization (SBO) based stacked autoencoder (SAE) is used for the classification of brain stroke. The design of optimal SAE using the SBO algorithm shows the novelty of the work. The performance of the presented technique was validated utilizing benchmark dataset which includes T2-weighted MR brain image collected from the axial axis with size of 256 × 256. The simulation outcomes indicated the promising efficiency of the proposed CAD-BSDC technique over the latest state of art approaches in terms of various performance measures.

Artificial intelligence for MRI stroke detection: a systematic review and meta-analysis

“…One study collected two datasets; one set was retrospective, and no information was provided for the other [37]. For stroke type, 24 reports studied ischaemic stroke, one studied haemorrhagic stroke [63], two had a dataset for both ischaemic and haemorrhagic stroke [40,49], one studied cerebral venous sinus thrombosis [63], and five reports did not elaborate on stroke type. Four studies performed multicentre data collection [36,37,39,40], but none of them had an external multicentre test set.…”

Enhancing accuracy in brain stroke detection: Multi-layer perceptron with Adadelta, RMSProp and AdaMax optimizers

“…This technique has multiple processes like feature extraction, hyperparameter tuning, etc. A data set of T2-weighted MR brain images was used for experimental analysis and demonstrated good efficiency as compared to state of art techniques in terms of different performance metrics ( Eshmawi et al, 2022 ). The authors explored ML algorithms for brain stroke classification using microwave imaging system and proposed a technique using distorted Born approximation for creating training data set on the basis of dielectric contrast space.…”

A Review on Image Classification Techniques for MRI Brain Stroke Lesion

“…It is useful for detecting hyperacute and acute stroke [8]. It provides great lesion contrast by measuring the diffusion of water molecules inside the tissue structure on a pixel level [9]. DWI has recently become the indication of MRI sequences in modern stroke lesion detection because it is able to reduce the diffusion water to detect the tissue of hyperintense pictures.…”