A METHODOLOGY TO IDENTFY BRAIN TUMOR USING DEEP LEARNING TECHNIQUES

Abstract

Patients suffering from brain tumors are some of the most prevalent and aggressive, and in the latter stages of the disease, they have a very low life expectancy. The planning stage of surgical procedures is very important if the goal is to provide patients a higher quality of life throughout the course of their lives. Imaging techniques such as computed tomography (CT), magnetic resonance imaging (MRI), and ultrasound are often used in the process of locating malignancies in various parts of the body, including the brain, lungs, liver, breast, and prostate. In this particular instance, magnetic resonance imaging (MRI) scans are carried out in order to examine the patient's brain in search of signs of cancer. On the other hand, since an MRI gets so much information at once, it is difficult to differentiate between a tumor and something that isn't a tumor at the same time. This approach has a lot of drawbacks, the most notable one being that it can only produce accurate quantitative data for a constrained selection of photographs. There are also a great deal of additional restrictions associated with it. It is feasible that automated systems that can be relied on in a trustworthy manner might aid in the prevention of suicide. It is difficult to automatically classify brain tumors since the region and structure around a tumor may be somewhat variable. This is one reason why brain tumors can be so dangerous. In this article, fresh techniques to the early identification of malignant brain tumors are explained. CNNs are put to use in order to classify the data (Convolutional Neural Networks). According on the site of the tumor, this section classifies gliomas, meningiomas, pituitary tumors, and other types of tumors that are not malignant. The architectural design of the system's deeper levels is predicated on the use of tiny kernels as the building blocks. This is a reference to the very little amount of mass that the neuron has. The fact that CNN's accuracy in test results was 99.5% puts it in a class by itself above all other methods used by the present generation. In addition to this, it is simple to understand and much simpler to put into practice.