Performance of Deep Convolutional Neural Networks for Classification of Acute Territorial Infarct on Brain MRI: A Pilot Study for Computer Vision in Stroke Neuroimaging

In the field of computer vision, CNNs are an evolving branch of deep learning algorithms that have attracted much attention as compared to the other deep learning methods. This is because of the intrinsic property of this group of networks that explicitly construct a hierarchical representation of input images, which result in a rich set of features for downstream classification tasks. CNNs generally contain three main layers such as a Convolutional Layer, a Pooling Layer, and a Fully-Connected Layer. These layers can be stacked together to form a full CNN architecture [1, 2]. CNNs have started to become a leading method for different applications of computer vision in medicine [3, 4].