Early Detection of Hemorrhagic Stroke Using a Lightweight Deep Learning Neural Network Model

Abstract

In present days, the major disease affecting people all across the world is “Cerebrovascular Stroke”. Computed tomographic (CT) images play a crucial role in identifying hemorrhagic strokes. It also helps in understanding the impact of damage caused in the brain cells more accurately. The existing research work is implemented on the Graphical Processing Unit (GPU’s) for stroke segmentation using heavyweight convolutions that require more processing time for diagnosis and increases the model's cost. Deep learning techniques with VGG-16 architecture and Random Forest algorithm are implemented for detecting hemorrhagic stroke using brain CT images under segmentation. A two-step light-weighted convolution model is proposed by using the data collected from multiple- repositories to inscribe this constraint. In the first step, the input CT images are given to VGG-16 architecture and in step two, data frames are given to random forest for stroke segmentation with three levels of classes. In this paper, we explore various training time values in the detection of stroke that reduces when compared with existing heavyweight models. Experimental results have shown that when compared to other existing architectures, our hybrid model VGG-16 and random forest achieved increased results obtained are dice coefficient with 72.92 and accuracy with 97.81% which shows promising results. © 2021 Lavoisier. All rights reserved.