In order to get around the inherent constraints of distinct imaging modalities, multimodal
fusion in neuroimaging integrates data from various imaging modalities. Higher temporal …

Today, medical images play a crucial role in obtaining relevant medical information for
clinical purposes. However, the quality of medical images must be analyzed and improved …

Medical imaging is a complex process that capitulates images created by X-rays, ultrasound
imaging, angiography, etc. During the imaging process, it also captures image noise during …

Medical imaging methods using multiple modalities are used more frequently in both clinical
settings and academic studies. The use of ensemble learning and associated multimodal …

Coronary Artery Disease (CAD) occurs when the coronary vessels become hardened and
narrowed, limiting blood flow to the heart muscles. It is the most common type of heart …

A new fusion method based on Multi-Focus Image Fusion Based on Discrete Wavelet
Transform with Deep Convolutional Neural Network (MFIF-DWT-CNN) is presented to …

Imaging data fusion is becoming a bottleneck in clinical applications and translational
research in medical imaging. This study aims to incorporate a novel multimodality medical …

The human voices are very flexible and contains a mess of sentiments or emotions. Feeling
or emotions in speech incorporates extra vision about human activities. Recognition of …

By integrating effective features of multi-modality medical images to provide richer
information, multi-modality medical image fusion has been substantially used in computer …

Mold level fluctuation has always been one of the key factors in slab quality control.
Abnormal fluctuations can easily cause slag entrainment and form surface defects of hot …