Deep feature extraction based brain image classification model using preprocessed images: PDRNet

Background: Stroke is a neurological condition that occurs when cerebral vessels become blocked and have reduced blood flow. This research proposes a hybrid deep feature-based feature engineering model to achieve high classification performance. Materials and method: In this research, three brain magnetic resonance image datasets were used to test the proposed model. A deep feature engineering model has been proposed to deploy the raw MRI and four pre-processing algorithms: GradCAM, histogram-matching, canny edge detection, and Locally Interpretable Model-Agnostic Explanations(LIME). The deep features have been extracted using Resnet101 and DenseNet201 pre-trained convolutional neural networks (CNN). Thus, this model is titled preprocessing based DenseNet and ResNet (PDRNet). The iterative neighborhood component analysis (INCA) function selects the most suitable features. These features are trained and validated using support vector machine (SVM) classifiers. Iterative Majority Voting (IMV) has been applied to the results obtained from the SVM. The best classification result has been selected by deploying IMV. Results: Our proposed PDRNet achieved a classification accuracy of 97.56% for Dataset 1, 99.32% for Dataset 2, and 99.16% for Dataset 3. The success of the presented model is demonstrated using these calculated accuracies. Conclusions: Our proposed hybrid deep feature model was tested on two datasets with two and four classes. It has also been compared to other state-of-art deep learning-based models, and our model performs better. These results and findings clearly demonstrate the success of the introduced hybrid deep feature engineering method.

Automated summary

This research proposes a hybrid deep feature-based feature engineering model for stroke classification. By applying multiple preprocessing algorithms and support vector machine classifiers, high accuracy stroke classification has been achieved.