Brain Tumor Detection and Tissue Classification Using Machine Learning

Brain Tumor Detection and Tissue Classification Using Machine Learning

Akshat Mohbe, Mehul Gaikwad, Rohit Chunge, Inderjeet Kaur Rooprai, Shilpa Sonawane

Electornics and Telecommunication & JSPM’s RSCOE Electornics and Telecommunication & JSPM’s RSCOE Electornics and Telecommunication & JSPM’s RSCOE Electornics and Telecommunication & JSPM’s RSCOE

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Abstract
Because of the complexity and diversity in tumours, this method is unfeasible for a bigger set of data, and the tumour detection procedure is a difficult effort. The paper's main goal is to use Magnetic Resonance Imaging (MRI), Image Pre-processing, and Machine Learning algorithms to create a model for detecting brain cancers. Human inspection is the traditional approach for finding cancers. This research attempts to create an automated method that can be used to determine whether or not a lump in the brain exists. Before an image is processed into an application, image pre-processing techniques are employed to increase its quality. For each MR picture,

pre-processing is utilized to extract image patches. The accuracy of proposed method is 95.50%. We have used a total number of 1720 samples out of which we have used 400 for testing our model.

Keywords - Magnetic Resonance Imaging, Brain tumor detection, Image Pre-processing, Machine learning.