The same sensor or a number of image sensors are used to take a series of photographs in
order to gather as much data as possible about the scene. Several imaging techniques are …

The integration of positron emission tomography (PET) and single-photon emission
computed tomography (SPECT) imaging techniques with machine learning (ML) algorithms …

Medical image fusion has brought practical applications in clinical diagnosis. However,
image fusion methods still face challenges because of problems with the quality of the input …

Synthesis of medical images is one of the indispensable tasks today because of its
applications in clinical diagnosis. Composite images often suffer from problems such as …

Medical image fusion is the process of combining information from multiple medical images
of the same body region acquired using different imaging modalities, such as computed …

Although current deep learning-based fault diagnosis methods have made great progress,
the accuracy of these models is usually attained based on many balanced training samples …

Existing infrared and visible image fusion methods suffer from edge information loss, artifact
introduction, and image distortion. Therefore, a dual-branch network model based on the …

Multi-modal medical image fusion (MMIF) integrates medical images of different modalities
into an image with rich information to boost the accuracy and efficiency of clinical diagnosis …

Multi-modal medical image fusion provides comprehensive and objective descriptions of
lesions for clinical medical assistance. However, retaining useful information while …

The development of methods based on artificial intelligence for the classification of medical
imaging is widespread. Given the high dimensionality of this type of images, it is imperative …