Predicting Brain Age using Machine Learning Algorithms : A Comprehensive Evaluation

Authors

  • Tanneeru Sudha Rani  Assistant Professor, Department of Information Technology, Bhoj Reddy Engineering College for Women, Hyderabad, India
  • M. Aparna  Department of Information Technology, Bhoj Reddy Engineering College for Women, Hyderabad, India
  • P. Bhavani  Department of Information Technology, Bhoj Reddy Engineering College for Women, Hyderabad, India

Keywords:

classification, convolutional neural network, feature extraction, machine learning, magnetic resonance imaging, segmentation, texture features.

Abstract

Medical imaging is gaining importance with an increase in the demand for automated, reliable, fast and efficient diagnosis which can provide insight into the image better than human eyes. The brain tumor is the second leading cause for cancer-related deaths in men age 20 to 39 and leading cause cancer among women in the same age group. Brain tumors are painful and should end in various diseases if not cured properly. The diagnosis of the tumor is a very important part of its treatment. Identification plays an important part in the diagnosis of benign and malignant tumors. A prime reason behind a rise in the number of cancer patients worldwide is the ignorance towards the treatment of a tumor in its early stages. This paper discusses such a machine learning algorithm that can write the user about the details of the tumor using brain MRI. These methods include noise removal and sharpening of the image along with basic morphological functions, erosion, and dilation, to obtain the background. Subtractions of background and its negative from different sets of images result in extracted in age. Plotting contour and c-label of the tumor and its boundary provides us with information related to the tumor that can help in a better visualization in diagnosing cases. This process helps in identifying the size, shape, and position of the tumor. It helps the medical staff as well the patient to understand the seriousness of the tumor with the help of different color-labeling for different levels of elevation. A GUI for the contour of the tumor and its boundary can provide information to the medical staff on the click of user choice buttons.

References

  1. Pankaj sapra, Rupinder pal Singh, Shivani Khurana, "brain tumor detection using neural network", international journal of science and modern engineering, ijisme, issn: 2319-6386, volume-1, issue-9, august 2013.
  2. Prachi gadpayleand, p.s. Mahajani, "detection and classification of brain tumor in MRI images ", international journal of emerging trends in electrical and electronics, ijetee – issn: 2320-9569, vol. 5, issue. 1, july-2013
  3. Bakes.s., Reyes.m., menze, b.: identifying the best machine learning algorithms for brain tumor segmentation, progression assessment, and overall survival prediction in the Brats challenge. In: arxiv:1811.02629 (2018)
  4. Bauer s., Nolte, Reyes.m., 2011. Fully automatic segmentation of brain tumor images using support vector machine classification in combination with hierarchical conditional random field regularization., in miccai, pp. 354–361.
  5. Prastawa m., Bullitt e., Greig, g., 2004. A brain tumor segmentation framework based on outlier detection. Medical image analysis 8, 275–283.
  6. Goodfellow, Warde-Farley, d., mirza, m., Courville, a., bengio, y., 2013b. Maxout networks, in icml.
  7. Gotz m., Weber c., Blocher j., Stieltjes b., Meinzer, h.p., Maier-Hein, k., 2014. Extremely randomized trees-based brain tumor segmentation, in proc of brats challenge - miccai.
  8. Cardoso, m. J., Sudre, c. H., Modat, m., Ourselin, s., 2015.a template-based multimodal joint generative model of brain data. In: information processing in medical imaging. Springer, pp. 17–29.
  9. Erihov, m., Alpert, s., Kiselev, p., Hashoul, s., 2015. A cross saliency approach to asymmetry-based tumor detection. In: medical image computing and computerassisted intervention–miccai 2015. Springer, pp. 636–643.
  10. Simonyan k., Zisserman a., 2014. Very deep convolutional networks for large-scale image recognition. Arxiv preprint arxiv:1409.1556
  11. M.Aarthilakshmi, is studying final year, Electrical and Electronics Engineering in National Engineering College, Kovilpatti.a. ([email protected])
  12. S. Meenakshi, is studying final year, Electrical and Electronics Engineering in National Engineering College, Kovilpatti. a. ([email protected])
  13. A.Poorna Pushkala, is studying final year, Electrical and Electronics Engineering in National Engineering College, Kovilpatti.([email protected])
  14. V.Rama@Ramalakshmi, is studying final year, Electrical and Electronics Engineering in National Engineering College, Kovilpatti. ([email protected])
  15. Dr.N.B.Prakash is working as Associate Professor in Electrical and Electronics Engineering at National Engineering College, Kovilpatti.([email protected])

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

Downloads

Published

2023-04-30

Issue

Volume 9, Issue 2, March-April-2023

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Tanneeru Sudha Rani, M. Aparna, P. Bhavani, " Predicting Brain Age using Machine Learning Algorithms : A Comprehensive Evaluation, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 9, Issue 2, pp.685-688, March-April-2023.