Melanoma is the most aggressive type of skin cancer, which significantly reduces the life
expectancy. Early detection of melanoma can reduce the morbidity and mortality associated …

Transfer learning is a widely used strategy in medical image analysis. Instead of only
training a network with a limited amount of data from the target task of interest, we can first …

Background Deep learning (DL) is the fastest-growing field of machine learning (ML). Deep
convolutional neural networks (DCNN) are currently the main tool used for image analysis …

Deep supervision, or known as' intermediate supervision'or'auxiliary supervision', is to add
supervision at hidden layers of a neural network. This technique has been increasingly …

In this paper, we address the problem of identifying brain haemorrhage which is considered
as a tedious task for radiologists, especially in the early stages of the haemorrhage. The …

Scene classification is a significant aspect of computer vision. Convolutional neural
networks (CNNs), a development of deep learning, are a well-understood tool for image …

Histopathological image analysis is the gold standard for pathologists to grade colorectal
cancers of different differentiation types. However, the diagnosis by pathologists is highly …

One way of resolving the problem of scarce and expensive data in deep learning for medical
applications is using transfer learning and fine-tuning a network which has been trained on …

Polyp recognition in colonoscopy images is crucial for early colorectal cancer detection and
treatment. However, the current manual review requires undivided concentration of the …

Medical image classification plays an important role in disease diagnosis since it can
provide important reference information for doctors. The supervised convolutional neural …