In recent years strokes are one of the leading causes of death by affecting the central nervous system. Among different types of strokes, ischemic and hemorrhagic majorly damages the central nervous system. According to the World Health Organization (WHO), globally 3% of the population are affected by subarachnoid hemorrhage, 10% with intracerebral hemorrhage, and the majority of 87% with ischemic stroke. In this research work, Machine Learning techniques are applied in identifying, classifying, and predicting the stroke from medical information. The existing research is limited in predicting risk factors pertained to various types of strokes. To address this limitation a Stroke Prediction (SPN) algorithm is proposed by using the improvised random forest in analyzing the levels of risks obtained within the strokes. This research of the Stroke Predictor (SPR) model using machine learning techniques improved the prediction accuracy to 96.97% when compared with the existing models.

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.

No abstract

No abstract

No abstract