Brain-Computer interfaces (BCIs) enhance the capability of human brain activities to interact
with the environment. Recent advancements in technology and machine learning algorithms …

Although deep learning models like CNNs have achieved great success in medical image
analysis, the small size of medical datasets remains a major bottleneck in this area. To …

In recent years, an increasing popularity of deep learning model for intelligent condition
monitoring and diagnosis as well as prognostics used for mechanical systems and …

With the exponential growth of medical image big data represented by high-resolution CT
images (CTI), the high-resolution CTI data is of great importance for clinical research and …

This paper presents an automatic content-based image retrieval (CBIR) system for brain
tumors on T1-weighted contrast-enhanced magnetic resonance images (CE-MRI). The key …

Recent years have witnessed increasing popularity and development of deep learning
spanning through various fields. Deep networks, and in particular convolutional neural …

Automatic liver and tumour segmentation in CT images are crucial in numerous clinical
applications, such as postoperative assessment, surgical planning, and pathological …

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 …

Content-based medical image retrieval (CBMIR) is one of the most challenging and
ambiguous tasks used to minimize the semantic gap between images and human queries in …

The need for time and attention given by the doctor to the patient, due to the increased
volume of medical data to be interpreted and filtered for diagnostic and therapeutic purposes …