Acta Scientific Computer Sciences

Research Article Volume 4 Issue 12

Brain Tumour Detection Using Convolutional Neural Network-XGBoost

BK Tripathy1*, RK Mohanty2 and SK Parida3

1School of Information Technology and Engineering, VIT, Vellore, Tamil Nadu, India
2School of Computer Science and Engineering, VIT, Vellore, Tamil Nadu, India
3Samanta Chandra Sekhar (Autonomous) College, Puri, Odisha, India

*Corresponding Author: BK Tripathy, School of Information Technology and Engineering, VIT, Vellore, Tamil Nadu, India.

Received: October 30, 2022; Published: November 15, 2022

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Abstract

Brain Tumour Detection is one of the most significant and essential task in the field of medical science. Brain tumours are of two types; malignant (cancerous) and benign (non-cancerous). Early detection followed by appropriate treatment is instrumental in increasing the probability of the patient getting cured. Manual methods are mostly time consuming, inaccurate leading to incorrect diagnosis. The main aim of this paper is to develop an automated system which can accurately detect the brain tumor with the help of a modified convolutional neural network algorithm called the CNN-XGBoost, which is an integrated and XGBoost, where CNN takes care of extracting training feature and XGBoost being used as the last level detector. The proposed method extracts the brain tumor from 2D Magnetic Resonance Images (MRI) of brain by using different algorithms like clustering, feature extraction, feature selection followed by CNN-XGBoost. Generally, this work is supposed to act as an assistive technology for doctors in order to make their work smoother and accurate. CNN is being implemented through Keras and TensorFlow. The most important task of this analysis is to distinguish between normal (without tumour) and abnormal (with tumour) images based on features, textures and image abnormalities. The proposed solution achieved high accuracy of identification at low computational cost.

Keywords: Convolutional Neural Network (CNN); XGBoost; Tumor Analysis; Feature Selection; Feature Extraction; Pattern Recognition; Magnetic Resource Image; Deep Learning

References

  1. Adate Amit and B K Tripathy. "A survey on deep learning methodologies of recent applications”. Deep Learning in Data Analytics. Springer, Cham, (2022): 145-170.
  2. Adate Amit and B K Tripathy. "S-lstm-gan: Shared recurrent neural networks with adversarial training”. Proceedings of the 2nd International Conference on Data Engineering and Communication Technology. Springer, Singapore, (2019): 107-115.
  3. Adate Amit and B K Tripathy. "Deep learning techniques for image processing”. Machine Learning for Big Data Analysis (2018): 69-90. DOI: 10.1515/9783110551433-00357
  4. Adate Amit., et al. "Impact of deep neural learning on artificial intelligence research”. Deep Learning Research and Applications, De Gruyter Publications (2020): 69-84.
  5. Aghdam Hamed Habibi and Elnaz Jahani Heravi. "Guide to convolutional neural networks”. New York, NY: Springer 10.978-973 (2017): 51.
  6. Ali Jehad., et al. "Random forests and decision trees”. International Journal of Computer Science Issues (IJCSI)5 (2012): 272.
  7. Bhardwaj Pranjal., et al. "Computational Biology in the Lens of CNN”. Handbook of Machine Learning Applications for Genomics. Springer, Singapore, (2022): 65-85.
  8. Bhattacharyya Siddhartha., et al. “Deep Learning: Research and Applications”. Vol. 7. Walter de Gruyter GmbH & Co KG, 7 (2020).
  9. Bose Ankita and B K Tripathy. "Deep learning for audio signal classification”. Deep Learning Research and Applications, De Gruyter Publications (2020): 105-136.
  10. Breiman Leo. "Random forests”. Machine Learning1 (2001): 5-32.
  11. Chen Tianqi and Carlos Guestrin. "Xgboost: A scalable tree boosting system”. Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining. (2016).
  12. Ciregan Dan., et al. "Multi-column deep neural networks for image classification”. 2012 IEEE conference on computer vision and pattern recognition. IEEE, (2012).
  13. Debgupta Rajdeep., et al. "A wide ResNet-based approach for age and gender estimation in face images”. International Conference on Innovative Computing and Communications. Springer, Singapore, (2020).
  14. Dong Hao., et al. "Automatic brain tumor detection and segmentation using U-Net based fully convolutional networks”. Annual conference on medical image understanding and analysis. Springer, Cham, (2017).
  15. Fasel Beat. "Robust face analysis using convolutional neural networks”. Object recognition supported by user interaction for service robots. IEEE, 2 (2002).
  16. Gupta Prajjwal., et al. "A Study of Gene Characteristics and Their Applications Using Deep Learning”. Handbook of Machine Learning Applications for Genomics. Springer, Singapore, (2022): 43-64.
  17. Hossain Tonmoy., et al. "Brain tumor detection using convolutional neural network”. 2019 1st international conference on advances in science, engineering and robotics technology (ICASERT). IEEE, (2019).
  18. Kasban Hany., et al. "A comparative study of medical imaging techniques”. International Journal of Information Science and Intelligent System2 (2015): 37-58.
  19. Kaul Deeksha., et al. "Deep learning in healthcare”. Deep Learning in Data Analytics. Springer, Cham, (2022): 97-115.
  20. Krizhevsky Alex., et al. "Imagenet classification with deep convolutional neural networks”. Communications of the ACM6 (2017): 84-90.
  21. Logeswari T and M Karnan. "An improved implementation of brain tumor detection using segmentation based on soft computing”. Journal of Cancer Research and Experimental Oncology1 (2010): 006-014.
  22. Maheshwari Karan., et al. "Convolutional neural networks: a bottom-up approach”. Deep Learning Research and Applications, De Gruyter Publications (2019): 21-50.
  23. Mustaqeem Anam., et al. "An efficient brain tumor detection algorithm using watershed & thresholding based segmentation”. International Journal of Image, Graphics and Signal Processing10 (2012): 34.
  24. Pathak Krishna., et al. "Classification of brain tumor using convolutional neural network”. 2019 3rd International conference on Electronics, Communication and Aerospace Technology (ICECA). IEEE, (2019).
  25. Prabha D Surya and J Satheesh Kumar. "Performance evaluation of image segmentation using objective methods”. Indian Journal of Science and Technology 8 (2016): 1-8.
  26. Prabhavathy P., et al. "Analysis of Diabetic Retinopathy Detection Techniques Using CNN Models”. Augmented Intelligence in Healthcare: A Pragmatic and Integrated Analysis. Springer, Singapore, (2022): 87-102.
  27. Rajasekaran Kavitha Angamuthu and Chellamuthu Chinna Gounder. "Advanced brain tumour segmentation from MRI images”. Basic Physical Principles and Clinical Applications, High-Resolution Neuroimaging (2018): 83-108.
  28. Ren Xudie., et al. "A novel image classification method with CNN-XGBoost model”. International Workshop on Digital Watermarking. Springer, Cham, (2017).
  29. Rungta Ravi Kumar., et al. "A Deep Learning Based Approach to Measure Confidence for Virtual Interviews”. International Conference on Computational Intelligence in Pattern Recognition. Springer, Singapore, (2022).
  30. Sihare, Pranchal., et al. "COVID-19 Detection Using Deep Learning: A Comparative Study of Segmentation Algorithms”. International Conference on Computational Intelligence in Pattern Recognition. Springer, Singapore, (2022).
  31. Singhania Udit and B K Tripathy. "Text-based image retrieval using deep learning”. Encyclopedia of Information Science and Technology, Fifth Edition. IGI Global, (2021): 87-97.
  32. Tripathy BK and J Anuradha. "Soft Computing-Advances and Applications”. Cengage Learning India Private Limited, New Delhi (2015).
  33. Tripathy B K., et al. "Brain MRI segmentation techniques based on CNN and its variants”. Brain Tumor MRI Image Segmentation Using Deep Learning Techniques. Academic Press, (2022): 161-183.
  34. Wu Ming-Ni., et al. "Brain tumor detection using color-based k-means clustering segmentation”. Third international conference on intelligent information hiding and multimedia signal processing (IIH-MSP 2007). IEEE, 2 (2007).
  35. Zhang Wei., et al. "Parallel distributed processing model with local space-invariant interconnections and its optical architecture”. Applied Optics32 (1990): 4790-4797.
  36. Brain Tumor: Statistics, Cancer.Net Editorial Board, 2/2022 (2022).
  37. General Information About Adult Brain Tumors. NCI. (2022).

Citation

Citation: BK Tripathy., et al. “Brain Tumour Detection Using Convolutional Neural Network-XGBoost". Acta Scientific Computer Sciences 4.12 (2022): 23-33.

Copyright

Copyright: © 2022 BK Tripathy., et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.




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