Brain Tumor Detection using CNN
A Deep Learning architecture that combines CNN (Convolution Neural Network), also referred to as NN (Neural Network), and VGG can be used to identify brain tumours, which is an uncontrollably expanding mass of tissue.
Abstract
The second most common cause of cancer today is a tumour. To detect tumours like brain tumour, the medical field needs a quick, automated, effective, and reliable technique. A crucial part of treatment is detection. Doctors keep a patient out of danger if accurate tumour detection is possible. This application makes use of several image processing methods. Using this application, doctors are able to properly treat a large number of tumour patients and save their lives. Simply put, a tumour is an accumulation of uncontrolledly expanding cells. The development of brain tumour cells causes brain failure because they eventually eat up all the nutrients meant for healthy cells and tissues. To determine the location and size of the patient's brain tumour, doctors manually review the patient's MR images of the brain. This is time-consuming and results in inaccurate tumour detection. A tumour is an uncontrollably expanding mass of tissue. A Deep Learning architecture that combines CNN (Convolution Neural Network), also referred to as NN (Neural Network), and VGG can be used to identify brain tumours. Transfer learning. Its main strength is the model's ability to predict whether a tumor is present in an image. Returning is yes if the cancer is present and no if it is not.