The recent development in the areas of deep learning and deep convolutional neural
networks has significantly progressed and advanced the field of computer vision (CV) and …

Computer vision and artificial intelligence applications in medicine are becoming
increasingly important day by day, especially in the field of image technology. In this paper …

The tremendous success of machine learning algorithms at image recognition tasks in
recent years intersects with a time of dramatically increased use of electronic medical …

Deep learning algorithms, in particular convolutional networks, have rapidly become a
methodology of choice for analyzing medical images. This paper reviews the major deep …

Today's medical imaging systems produce a huge amount of images containing a wealth of
information. However, the information is hidden in the data and image analysis algorithms …

Convolutional neural networks, are one of the most representative deep learning models.
CNNs were extensively used in many aspects of medical image analysis, allowing for great …

Medical image interpretation is an essential task for the correct diagnosis of many diseases.
Pathologists, radiologists, physicians, and researchers rely heavily on medical images to …

Artificial intelligence tools, particularly convolutional neural networks (CNNs), are
transforming healthcare by enhancing predictive, diagnostic, and decision-making …

Training a deep convolutional neural network (CNN) from scratch is difficult because it
requires a large amount of labeled training data and a great deal of expertise to ensure …

CNN is inspired from Primary Visual (V1) neurons. It is a typical deep learning technique
and can help teach machine how to see and identify objects. In the most recent decade …