Research on the performance of self-made capsule networks on pneumonia dataset

Capsule Networks (CapsNets) were proposed by Geoffrey Hinton and his team, pioneers in the field of deep learning, in 2017 to address certain limitations of traditional Convolutional Neural Networks (CNNs). The core innovation of Capsule Networks lies in their representation of objects within images. They not only identify the presence of objects but also capture spatial relationships and pose information, thus offering a richer and more equivariant feature representation. However, existing literature in this area remains relatively scarce. This paper aims to conduct a brief preliminary exploration using Capsule Networks for a medical binary classification task on a pneumonia dataset.