Brain Tumor Detection Using Medical Image Processing and Convolution Neural Network
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 is 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. There are several existing of techniques are available for
brain tumor segmentation and classification to detect the brain tumor. There
are many techniques available presents a study of existing techniques for brain
tumor detection and their advantages and limitations. To overcome these
limitations, propose a Convolution Neural Network (CNN) based classifier. CNN
based classifier does the comparison between trained and test data, from this
to get the simplest result.
Country : India
1 Sagarika Saka
Assistant Professor, Department of Computer Science And Engineering, Malla Reddy College of Engineering for Women, Hyderabad -500100, Telangana, India
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