In medical image processing, brain tumor detection and segmentation is a challenging and
time-consuming task. Magnetic Resonance Image (MRI) scan analysis is a powerful tool in …

Since the advent of U-Net, fully convolutional deep neural networks and its many variants
have completely changed the modern landscape of deep-learning based medical image …

Extracting knowledge from digital images largely depends on how well the mining
algorithms can focus on specific regions of the image. In multimodality image analysis …

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 …

Because of the large variabilities in brain tumors, automating segmentation remains a
difficult task. We propose an automated method to segment brain tumors by integrating the …

A brain tumor is a portion of uneven cells, need to be detected earlier for treatment. Magnetic
Resonance Imaging (MRI) is a routinely utilized procedure to take brain tumor images …

Due to the inherent robustness of segmentation models, traditional norm-bounded attack
methods show limited effect on such type of models. In this paper, we focus on generating …

Effective treatment of brain tumours depends on early identification of tumour tissues.
Categorizing tumors is essential for its early identification. In order to split and categorise …

The segmentation of brain tumors is an important component in medical image analysis.
Early detection of brain tumors improves diagnostic methods and increases patients' …

Accurate brain tumor segmentation is an interesting and challenging task of magnetic
resonance imaging (MRI) in the field of medical image processing. For this purpose, we …