Internet of Medical Things (IoMT) can connect many medical imaging equipment to the
medical information network to facilitate the process of diagnosing and treating doctors. As …

The need for medical image encryption is increasingly pronounced, for example, to
safeguard the privacy of the patients' medical imaging data. In this article, a novel deep …

Medical practitioners generally rely on multimodal brain images, for example based on the
information from the axial, coronal, and sagittal views, to inform brain tumor diagnosis …

Brain tumor segmentation using MRI data remains challenging for some reasons. Hence,
how to accurately segment the brain tumor is kept as a significant topic in the area of …

The deep learning method has shown its outstanding performance in object recognition and
becomes the first choice for medical image analysis. However, how to effectively propagate …

Ultrasound-guided injection is widely used to help anesthesiologists perform anesthesia in
peripheral nerve blockade (PNB). However, it is a daunting task to accurately identify nerve …

This paper targets a novel classifier merging methodology for automated and precise
judgement of Alzheimer's disease. The six diverse joining rules (mean, median, product …

Structural magnetic resonance imaging (sMRI) is a popular technique that is widely applied
in Alzheimer's disease (AD) diagnosis. However, only a few structural atrophy areas in sMRI …

The application of a radioactive tracer and following brain Single Positron Emission
Computed Tomography (SPECT) is a standard technique used in neurodegenerative …

Glioma is the most common tumor in the brain's central nerve cells, which is extremely
dangerous clinically. Glioma's accurate surgical localization and diagnosis both rely on the …