BRAIN TUMOR DETECTION USING MACHINE LEARNING

Abstract

With the requirement for automated, reliable, rapid, and efficient diagnostics that can deliver superior
picture understanding than human eyes, medical imaging is growing. Brain tumours are the second highest
cause of cancer mortality in males aged 20–39 and the first in women. Brain tumors hurt and may lead to many
disorders if untreated. Tumor diagnosis is crucial to therapy. Identification helps diagnose benign and malignant
cancers. Ignorance about early tumor therapy is a major cause of cancer's global growth. This research proposes
a machine learning technique that uses brain MRI to write tumor information to the user. Image noise reduction,
sharpening, basic morphological operations, erosion, and dilation create the backdrop. Age is derived by
subtracting background and negative from distinct photos. Plotting the tumor's shape and c-label and boundaries
may help us see and diagnose cases. This helps determine tumor size, shape, and location. It helps doctors and
patients comprehend the tumor's severity using color-coding for elevation levels. A GUI for the tumor's outline
and boundaries may educate medical professionals via user choice buttons.