Brain Tumor Prediction Using Deep Learning

Brain Tumor Prediction Using Deep Learning

Mrs. Nandhini A1, Sathiyaseelan M2
1Assistant Professor (SG), Department of Computer Applications, Nehru college of management,

Coimbatore, Tamilnadu, India.

2 II MCA, Department of Computer Applications, Nehru college of management,

Coimbatore, Tamilnadu, India.

Abstract:

Brain tumors are the most aggressive and deadly disease of the central nervous system; if they are discovered at the highest stage, they could be fatal. Therefore, preventing all risk factors and improving quality of life depend heavily on early identification of brain tumors. In general, a variety of computer- based methods are employed, beginning with CT scans, ultrasounds, and magnetic resonance imaging (MRI). In order to identify brain tumors, MRI scans are used, and those pictures are inspected to determine whether they are tumorous or not. However, detecting and classifying the tumor becomes challenging and time-consuming when dealing with vast amounts of data, especially when human error is taken into account. Thus, the convolutional neural network (CNN) technique was introduced in this research to detect brain tumors in order to reduce all of these effects. CNN is a Deep Learning approach that doesn't require a subject-matter expert to put it into action.

Results: CNN requires a wide range of data in order to achieve superior picture classification. In this case, we are training a set of data, comparing it with a test set, then classifying and detecting brain tumors based on the results.

Keywords: brain tumor, convolutional neural network, and magnetic resonance imaging (MRI).