Brain tumour identification with traditional magnetic resonance imaging (MRI) tends to be
time-consuming and in most cases, reading of the resulting images by human agents is …

Objectives Medical image analysis practices face challenges that can potentially be
addressed with algorithm-based segmentation tools. In this study, we map the field of …

This cutting-edge volume, Computer Vision and Recognition Systems: Research
Innovations and Trends, focuses on how artificial intelligence can be used to give computers …

Segmentation methods can mutually exclude the location of the tumor. However, the
challenge of complex location or incomplete identification is located in segmentation …

Inaccuracies in anomaly prediction have become an alarming issue in the field of medical
image analysis, and these quandaries have burgeoned due to the errors caused by the …

The application of image processing in medical image analysis offers physicians numerous
advantages in diagnosing and predicting patient recovery. A typical task that requires …

Growth of cancer cells within the human body is a major outcome of the manipulation of cells
and it has resulted in the deterioration of the life span of humans. The impact of cancer cells …

Computer vision methodologies has been predominantly used for solving number of
problems related with pattern recognition, machine vision, object detection, object …

Background Early prognosis of a brain tumour may offer better life expectancy. Magnetic
Resonance Imaging (MRI) coupled with an efficient machine learning segmentation …

This paper discusses a fuzzy-directed graph-based theoretical technique for image analysis.
Due to the property that, in a fuzzy graph, the membership grade of the edges must be less …