Brain Stroke Detection Using DenseNet Algorithm

A brain stroke occurs when blood flow to a part of the brain is interrupted or reduced, depriving brain tissue of oxygen and nutrients. This can cause brain cells to die, leading to potential loss of function depending on the affected area. A significant amount of research has focused on using Convolutional Neural Networks (CNNs) for brain stroke detection, leveraging their ability to automatically extract features from medical images. In this paper, we will employ the DenseNet201 architecture in performing differential diagnosis from normal to stroke brain conditions using CT scans data. First and foremost, we would like to outline what is basically the key objective of developing a binary classification model that can accurately determine the presence or absence of stroke. The dataset consists of CT scans divided into two classes: normal and stroke. This model utilizes DenseNet201— which draws its power from extremely efficient use of parameters and connections—by densely feeding each other in the learning of detailed features from CT scans. Since performance varies significantly on complexity and subtle variation with fine-tuning of the model in medical images, this research is therefore significant in examining the prospects of using deep learning-based models, such as DenseNet201, to assist with stroke detection and diagnosis. Data Set is named as Brain_Stroke_CT-SCAN_image and gained an accuracy as 93%.