Nowadays, cancer has become a major threat to people's lives and health. Convolutional
neural network (CNN) has been used for cancer early identification, which cannot achieve …

Deep learning techniques have effectively treated about one million gastrointestinal patients
in recent years. It is the most advanced medical imaging technique for the diagnosis of …

Abstract Capsule Networks (CapsNets) have demonstrated to be a promising alternative to
Convolutional Neural Networks (CNNs). However, they often fall short of state-of-the-art …

Early diagnosis of patients' disease is crucial since it helps doctors and patients devise a
treatment plan. Therefore, recognizing medical images using Artificial intelligence-based …

A key component to the success of deep learning is the availability of massive amounts of
training data. Building and annotating large datasets for solving medical image classification …

We propose a novel architecture for object classification, called Self-Attention Capsule
Networks (SACN). SACN is the first model that incorporates the Self-Attention mechanism as …

Capsule network is a novel architecture to encode the properties and spatial relationships of
the feature in an image, which shows encouraging results on image classification. However …

Capsule network (CapsNet) is well-known as an evolution of classical convolution neural
network (CNN), which is good at recognizing the postures, orientations, and textures, thus …

In order to diagnose and treat diseases, a variety of imaging techniques are used, including
X-ray, computed tomography (CT), mammography, ultrasound, and magnetic resonance …

Convolutional Neural Networks (CNNs) have achieved promising results in medical image
segmentation. However, CNNs require lots of training data and are incapable of handling …