Deep Learning based Brain Stroke Detection using Improved VGGNet

Abstract

Brain stroke is one of the critical health issues as the after effects provides physical inability and sometimes death. The inability of focus in the brain due to bleeding or clogged blood vessels leads to stroke. Early treatment and diagnosis are crucial in and following manual procedures takes more time which further increases the criticalness. Artificial intelligence and machine learning techniques hands together in medical domain and numerous applications are developed to reduce the diagnosis time and to improve the accuracy. Incorporating machine learning techniques in brain stroke detection is a familiar research arena and numerous research works are evolved with better solutions. However, the drive towards developing better system for brain stroke detection is still in progress. Thus, in this research work, deep learning-based brain stroke detection system is presented using improved VGGNet. Simulation analysis using a set of brain stroke data and the performance of learning algorithms are measured in terms of accuracy, sensitivity, specificity, precision, f-measure, and Jaccard index. The better performance of proposed model is comparatively analyzed with traditional machine learning algorithms like support vector machine, Naïve Bayes, Decision tree, K-Nearest neighbor, and recent deep learning models like ResNet, Squeeze Net, Alex Net, and Google Net algorithms. Experimental results validates that the Improved VGG model attained better performance for all the parameters. Specifically with 96.86% of detection accuracy improved VGG model detects the brain strokes effectively compared to other learning algorithms.