Optimization of Brain Cancer Images with Some Noise Models

Abstract

Diagnosis of brain cancer based on magnetic resonance images (MRI) is affected by many conditions, such as the movement of the patient during capture, which leads to the occurrence of noise associated with these images. In order to improve the corner detection methods, many corner detection methods were adopted according to the many noise models. Experimental results showed the effect of magnetic resonance imaging capabilities on both Corner detection methods, noise model. It is possible to rely on other medical images such as Doppler images and neural network algorithms to use image features in diagnosing cancer.