Brain Tumor Detection Using Deep Learning
The main purpose of this research paper is to identify irregular sample images and find the tumor area and the abnormal sections of images will forecast the levels of tumors so that proper treatment can be done.
Abstract
Identifying brain tumors and improving patient care are the goals of this project. Brain malignant tumors, which are aberrant cell growths, are known as tumors. Frequently, infectious brain tissues are caught by CT and MRI scans. There are many other methods that are used for the detection of brain tumors and some of them are positive charges imaging and cerebral X-ray photography of blood or lymph vessels and tests at the molecular level. So, this paper will use different MRI images to detect ailment cause like tumors. The main purpose of this research paper is to 1) identify irregular sample images and 2) find the tumor area. The abnormal sections of images will forecast the levels of tumors so that proper treatment can be done. Deep learning is used to find abnormal regions from sample images. This paper will use VGG-16 to segment the abnormal part. The density of the infected region is defined by the number of pixels with malignancy.