Brain Tumor Detection Using Deep Learning

Abstract

The Using medical imaging to segment brain tumorsis a particularly challenging but crucial process. Thisis because of the potential for erroneous prognosis anddiagnosis as a result of manual classification. Workingwith vast volumes of data also requires a lot of work.Because of their similar appearances and wide rangeof traits, It might be difficult to tell a brain tumororiginating from healthy tissue in photographs. In thisstudy, fuzzy C-Means was used to remove brain tumorsfrom two-dimensional MRI images. CNN and classicalclassifiers were then used.The study’s dataset includedtumors of various sizes, shapes, and intensities. KNN,MLP, LR, SVM, and Naive.The typical classifier of thescikit-learn module included popular machine learningmethods including Bayes, Random Forest, and others.Abrain tumor is a condition brought on by aberrant braincell proliferation. Brain tumors can be classified as eitherbenign or malignant, with the former being classified as acancerous brain tumor. It is difficult to forecast the survivalrate of a patient who has a tumor because of the rarityand variety of tumor kinds. Fifteen out of one hundredindividuals with brain cancer had a possibility of survivingfor ten years or longer after diagnosis, per a UK cancerstudy. The type of tumor, the location of the tumor inthe brain, the abnormalities of the cells, and Additionalelements all influence how a patient with a brain tumor istreated