Automatic Detection of Brain Strokes in CT Images Using Soft Computing Techniques

Stroke is the cerebrovascular issue influencing blood supply to the mind that predominantly influences individuals over 65 years old. This article proposes an automatic technique to perceive and orchestrate the sorts of strokes starting with 2D cerebrum CT images. The methodology is divided into four steps. In the introductory step, preprocessing may be performed on the image to expel unwanted disturbance by applying median filtering. In second step, different texture-based features are extricated utilizing wavelet packet transform (WPT) for classification. In the following step, Linear Discriminant Analysis (LDA) is utilized to diminish the dimensionality of the features. Finally, the diminished group of feature is connected to the supervised learning techniques for classification of normal and infected region. The goal of the proposed work is to build up a framework that accurately extracts the stroke region from CT images that helps doctors in their diagnosis decisions. The performance of the proposed scheme has fundamentally enhanced the stroke classification precision contrasted with other neural system-based classifier.