Deep learning (DL) methods have recently become state-of-the-art in most automated
medical image segmentation tasks. Some of the biggest challenges in this field are related …

Ocular diseases are eventually increasing these days that cause partial or complete vision
loss even at an early age, the prominent reason behind this is cardiovascular diseases that …

The lung is a critical organ for blood gas exchange. Early lung disease detection is often
hindered by subtle symptoms. The diagnosis typically requires expert analysis of lung image …

Everyone observed the unbearable hardship of the coronavirus. The necessity of automatic
coronavirus detection has been realized from the dire circumstances. Among the various …

Brain tumors are a leading cause of neurological impairment, often resulting in severe
consequences, including fatality. Timely detection of brain tumors is imperative for effective …

The development of mobile and on the edge applications that embed deep convolutional
neural models has the potential to revolutionise healthcare. However, most deep learning …

U-shaped encoder-decoder models have excelled in automatic medical image
segmentation due to their hierarchical feature learning capabilities, robustness, and …

Landslide susceptibility prediction usually involves the comprehensive analysis of terrain
and other factors that may be distributed with spatial patterns. Without considering the …

Automatic identification and segmentation of lesions in medical images has become a focus
area for researchers. Segmentation for medical image provides professionals with a clearer …

In the realm of medical imaging, the precise segmentation and classification of gliomas
represent fundamental challenges with profound clinical implications. Leveraging the BraTS …