Deep Learning Framework to Detect Ischemic Stroke Lesion in Brain MRI Slices of Flair/DW/T1 Modalities

Abstract

Ischemic stroke lesion (ISL) is a brain abnormality. Studies proved that early detection and treatment could reduce the disease impact. This research aimed to develop a deep learning (DL) framework to detect the ISL in multi-modality magnetic resonance image (MRI) slices. It proposed a convolutional neural network (CNN)-supported segmentation and classification to execute a consistent disease detection framework. The developed framework consisted of the following phases; (i) visual geometry group (VGG) developed VGG16 scheme supported SegNet (VGG-SegNet)-based ISL mining, (ii) handcrafted feature extraction, (iii) deep feature extraction using the chosen DL scheme, (iv) feature ranking and serial feature concatenation, and (v) classification using binary classifiers. Fivefold cross-validation was employed in this work, and the best feature was selected as the final result. The attained results were separately examined for (i) segmentation; (ii) deep-feature-based classification, and (iii) concatenated feature-based classification. The experimental investigation is presented using the Ischemic Stroke Lesion Segmentation (ISLES2015) database. The attained result confirms that the proposed ISL detection framework gives better segmentation and classification results. The VGG16 scheme helped to obtain a better result with deep features (accuracy > 97%) and concatenated features (accuracy > 98%).

Artificial Intelligence for Automated DWI/FLAIR Mismatch Assessment on Magnetic Resonance Imaging in Stroke: A Systematic Review

“…One was not in English. Two had outcomes not relevant for this study, i.e., a focus on prediction accuracy for upper extremity motor outcomes 90 days post-stroke and a focus on detecting FLAIR and DWI lesions independently and not as a mismatch [ 20 , 21 ]. One article was a preprint of an already-included article.…”

Deep Learning and Artificial Intelligence in Action (2019–2023): A Review on Brain Stroke Detection, Diagnosis, and Intelligent Post-Stroke Rehabilitation Management

“…Table III highlights the layer types and number of layers contained in some of the extensively utilized DL models in the literature for handling brain stroke data. [26] 44 (40 3D convolution layers, 2 fully connected layers, 1 ReLu, 1 SoftMax layer) VGG-SegNet [47] 40 (2 blocks of 2 convolutional + max pooling layer, 3 blocks of 3 convolutional + max pooling layer, 3 sets of max-pooling + 3 up sampling layer, 2 blocks of max pooling + 2 up sampling layer, 3 fully connected layer, 1 SoftMax layer) VGG16 [5] 16 (2 blocks of 2 convolutional + max pooling layer, 3 blocks of 3 convolutional + max pooling layer, 1 flatten layer, 1 dense layer) 1D-CNN [10] 16 (4 blocks of 2 convolutions + 1 max-pooling layer, 1 global average poling layer, 1 dropout layer, 1 fully connected layer, 1 softmax layer) OzNet [27] 34 (7 blocks of a convolutional + a maximum pooling + a ReLU + a batch normalization layer, 2 fully connected layers, a dropout layer, a SoftMax layer, and a classification layer) ISP-Net [33] 22 (4 blocks of convolution + batch normalization + ReLu layer, 3 max-pooling layers, 5 residual blocks, 2 deconvolution layers) CNN [40] 13 (5 blocks of convolution + max-pooling layers, a flatten layer, 2 fully connected layers) AG-DCNN [67] 23 (2 convolution + max-pooling layer, 3 blocks of convolution + max-pooling layer, 3 blocks of a upsampled layer + 3 convolution + a max-pooling layer) PerfU-Net [17] 30 Gaidhani, B. R. et al, [50] used MRI-based brain stroke diagnosis utilizing CNN and DL algorithms. The suggested technique is to use semantic segmentation to identify MRI brain stroke images as abnormal or normal and to define aberrant areas.…”

Efficient segmentation of active and inactive plaques in FLAIR-images using DeepLabV3Plus SE with efficientnetb0 backbone in multiple sclerosis

“…Moreover, another study introduces a deep learning method for the automatic identification of ischemic stroke lesions in brain MRI FLAIR images. This employs CNN-driven segmentation and classification, providing a consistent framework for disease detection 31 . Additional research work focuses on a CNN segmentation approach aimed at extracting MS lesions from 2D brain MRI slices, significantly refining MS detection through the utilization of the Visual Geometry Group (VGG) U-Net architecture 32 .…”