Brain Tumor Detection and Classification Using Convolutional Neural Network (CNN)

Aim:

                  To detect and identify the Brain Tumor using Deep-Learning techniques.

Abstract:

            Medical image processing is that the one among the foremost demanding and promising field nowadays. Tumor is a rapid uncontrolled growth of cell. The tumor are often classified as benign, malignant and premalignant. When a tumor is noticed as malignant then the tumor results in cancer. Earlier stage of tumor is used to be detected manually through observation of image by doctors and it takes more time and sometimes gets inaccurate results. Today different computer added tool is employed in medical field. These tools provide a quick and accurate result. Magnetic Resonance Images (MRI) is the most widely used imaging technique for analyzing internal structure of human body. The MRI is used even in diagnosis of most severe disease of medical science like brain tumors. The brain tumor detection process consist of image processing techniques involves four stages. Image pre-processing, image segmentation, feature extraction, and finally classification.

Proposed System
             Automated brain tumor detection is extremely necessary as high accuracy is required when human life is involved. Automated detection of tumor in MR images involves feature extraction and classification using machine learning algorithm. Our approach consists of three steps: (A) Brain image pre-processing, (B) Brain feature extraction, (C) brain tumor classification using web application. The input of the approach is that the brain images and therefore the output are the respective sort of the brain tumor.