Deep learning-based methods have become the dominant methodology in medical image
processing with the advancement of deep learning in natural image classification, detection …

In the last decade, research on artificial intelligence has seen rapid growth with deep
learning models, especially in the field of medical image segmentation. Various studies …

Encoder-decoder networks are state-of-the-art approaches to biomedical image
segmentation, but have two problems: ie, the widely used pooling operations may discard …

In this paper, we present a fully automatic brain tumor segmentation and classification model
using a Deep Convolutional Neural Network that includes a multiscale approach. One of the …

Brain tumors (BTs) are spreading very rapidly across the world. Every year, thousands of
people die due to deadly brain tumors. Therefore, accurate detection and classification are …

Brain tumor diagnosis and classification still rely on histopathological analysis of biopsy
specimens today. The current method is invasive, time-consuming and prone to manual …

Brain tumor classification is a very important and the most prominent step for assessing life‐
threatening abnormal tissues and providing an efficient treatment in patient recovery. To …

Background and Objectives: Clinical diagnosis has become very significant in today's health
system. The most serious disease and the leading cause of mortality globally is brain cancer …

Simple Summary In this research, we addressed the challenging task of brain tumor
detection in MRI scans using a large collection of brain tumor images. We demonstrated that …

Brain tumor is one of the most dangerous cancers in people of all ages, and its grade
recognition is a challenging problem for radiologists in health monitoring and automated …