Medical imaging is a useful tool for disease detection and diagnostic imaging technology
has enabled early diagnosis of medical conditions. Manual image analysis methods are …

Compared with common deep learning methods (eg, convolutional neural networks),
transfer learning is characterized by simplicity, efficiency and its low training cost, breaking …

Objective Employing transfer learning (TL) with convolutional neural networks (CNNs), well-
trained on non-medical ImageNet dataset, has shown promising results for medical image …

The advent of deep learning has brought great change to the community of computer
science and also revitalized numerous fields where traditional machine learning methods …

Background Transfer learning (TL) with convolutional neural networks aims to improve
performances on a new task by leveraging the knowledge of similar tasks learned in …

Deep learning requires a large amount of data to perform well. However, the field of medical
image analysis suffers from a lack of sufficient data for training deep learning models …

This survey aims to identify commonly used methods, datasets, future trends, knowledge
gaps, constraints, and limitations in the field to provide an overview of current solutions used …

Deep learning methods have huge success in task specific feature representation. Transfer
learning algorithms are very much effective when large training data is scarce. It has been …

Over recent years, deep learning (DL) has established itself as a powerful tool across a
broad spectrum of domains in imaging—eg, classification, prediction, detection …

Featured Application The proposed intelligent medical system is applicable for a medical
diagnostic system, especially for the diagnosis of diabetic foot ulcer. Abstract One of the …