Title: MACHINE INTELLIGENCE FOR BRAIN SEGMENTATION: A TOOL TO
IDENTIFY BRAIN ILLNESSES THROUGH SEGMENTATION
Authors: Vikranth Nara, Harshit Pottipati and Rishi Athavale
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Vikranth Nara, Harshit Pottipati and Rishi Athavale
Rock Ridge High School, 43460 Loudoun Reserve Dr, Ashburn, VA, United States of America
Rock Ridge High School, 43460 Loudoun Reserve Dr, Ashburn, VA, United States of America
MLA 8 Nara, Vikranth, et al. "MACHINE INTELLIGENCE FOR BRAIN SEGMENTATION: A TOOL TO IDENTIFY BRAIN ILLNESSES THROUGH SEGMENTATION." Int. j. of Social Science and Economic Research, vol. 7, no. 9, Sept. 2022, pp. 3115-3125, doi.org/10.46609/IJSSER.2022.v07i09.023. Accessed Sept. 2022.
APA 6 Nara, V., Pottipati, H., & Athavale, R. (2022, September). MACHINE INTELLIGENCE FOR BRAIN SEGMENTATION: A TOOL TO IDENTIFY BRAIN ILLNESSES THROUGH SEGMENTATION. Int. j. of Social Science and Economic Research, 7(9), 3115-3125. Retrieved from https://doi.org/10.46609/IJSSER.2022.v07i09.023
Chicago Nara, Vikranth, Harshit Pottipati, and Rishi Athavale. "MACHINE INTELLIGENCE FOR BRAIN SEGMENTATION: A TOOL TO IDENTIFY BRAIN ILLNESSES THROUGH SEGMENTATION." Int. j. of Social Science and Economic Research 7, no. 9 (September 2022), 3115-3125. Accessed September, 2022. https://doi.org/10.46609/IJSSER.2022.v07i09.023.
References [1]. MRI for cancer. American Cancer Society. (n.d.). Retrieved September 27, 2022, from https://www.cancer.org/treatment/understanding-your-diagnosis/tests/mri-for-cancer.html
[2]. Inderscience. (2009, September 27). Tired Doctors Make More Mistakes. ScienceDaily. Retrieved September 26, 2022 from
www.sciencedaily.com/releases/2009/09/090914111307.htm
[3]. Developer. (2021, September 28). Facts about mris. Envision Radiology. Retrieved September 27, 2022, from https://www.envrad.com/facts-about-mris/
[4]. Medical Segmentation Decathlon. (n.d.). Retrieved September 27, 2022, from http://medicaldecathlon.com/
[5]. Net: Convolutional networks for biomedical image segmentation. (n.d.). Retrieved May 01, 2022 from https://lmb.informatik.uni-freiburg.de/people/ronneber/u-net/
ABSTRACT: Machine Learning is becoming a prominent force in the medical field. We created Machine
Intelligence for Brain Segmentation (MIBS), a tool that segments brain MRIs into different
colors that signify enhancing tumors, non-enhancing tumors, and edema. The dataset used was of
624 MRIs from the Medical Segmentation Decathlon. The model was trained with the U-Net
algorithm, a Convolutional Neural Network made for Biomedical Image Segmentation, and
resulted in an average accuracy of ~99% across the different classes and ~0.75 an average F1-
score across the different classes.
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