77
Views
0
CrossRef citations to date
0
Altmetric
Research Article

Efficient Machine Learning-based Approach for Brain Tumor Detection Using the CAD System

, , , &

REFERENCES

  • G. Nidhi, and P. Khanna, “A non-invasive and adaptive CAD system to detect brain tumor from T2-weighted MRIs using customized Otsu’s thresholding with prominent features and supervised learning,” Signal Process., Image Commun., Vol. 59, pp. 18–26, 2017.
  • M. A. Javid, and S. A. Buzdar, “A novel computer aided diagnostic system for quantification of metabolites in brain cancer,” Biomed. Signal. Process. Control., Vol. 66, pp. 102–401, 2021.
  • Y.-W. Liang, W.-Y. Chen, J.-L. Lee, and L.-C. Huang, “Nurse staffing, direct nursing care hours and patient mortality in Taiwan: The longitudinal analysis of hospital nurse staffing and patient outcome study,” BMC Health Serv. Res., Vol. 12, no. 1, p. 44, 2012.
  • Y. Xue, and H. Liang, “Analysis of telemedicine diffusion: The case of China,” IEEE Trans. Inf. Technol. Biomed., Vol. 11, no. 2, pp. 231–233, 2007.
  • H. Wang, S. N. Ahmed, and M. Mandal, “Computer-aided diagnosis of cavernous malformations in brain MR images,” Comput. Med. Imaging Graph., Vol. 66, pp. 115–123, 2018.
  • C. Kaushal, S. Bhat, D. Koundal, and A. Singla, “Recent trends in computer assisted diagnosis (CAD) system for breast cancer diagnosis using histopathological images,” IRBM, Vol. 40, no. 4, pp. 211–227, 2019.
  • R. Uppada, S. P. Kodati, and S. K. Rao, “Automated computer aided diagnosis using altered multi-phase level sets in application to categorize the breast cancer biopsy images,” IETE. J. Res., pp. 1–15, 2021.
  • T. Ha, Y. Jung, J. Y. Kim, S. Y. Park, D. K. Kang, and T. H. Kim, “Comparison of the diagnostic performance of abbreviated MRI and full diagnostic MRI using a computer-aided diagnosis (CAD) system in patients with a personal history of breast cancer: The effect of CAD-generated kinetic features on reader performance,” Clin. Radiol., Vol. 74, no. 10, pp. 817.e15–817.e21, 2019.
  • H. E. M. Abdalla, and M. Y. Esmail, “Brain tumor detection by using artificial neural network,” in 2018 International Conference on Computer, Control, Electrical, and Electronics Engineering (ICCCEEE), IEEE, 2018.
  • B. Devkota, A. Alsadoon, P. W. C. Prasad, A. K. Singh, and A. Elchouemi, “Image segmentation for early stage brain tumor detection using mathematical morphological reconstruction,” Procedia. Comput. Sci., Vol. 125, pp. 115–123, 2018.
  • M. Zhang, et al., “Deep-learning detection of cancer metastases to the brain on MRI,” J. Magn. Reson. Imaging, Vol. 52, no. 4, pp. 1227–1236, 2020.
  • A. Naseer, T. Yasir, A. Azhar, T. Shakeel, and K. Zafar, “Computer-aided brain tumor diagnosis: Performance evaluation of deep learner CNN using augmented brain MRI,” Int. J. Biomed. Imaging., Vol. 2021, p. 11, 2021. https://doi.org/10.1155/2021/5513500.
  • M. Tamilarasi, “Performance analysis of glioma brain tumor segmentation using CNN deep learning approach,” IETE. J. Res., pp. 1–12, 2021.
  • P. Jasmine, and T. S. Sivarani, “Retracted article: Computer aided diagnosis of brain tumor using novel classification techniques,” J. Ambient. Intell. Humaniz. Comput., Vol. 12, no. 7, pp. 7499–7509, 2021.
  • A. S. Musallam, A. S. Sherif, and M. K. Hussein, “A new convolutional neural network architecture for automatic detection of brain tumors in magnetic resonance imaging images,” IEEE. Access., Vol. 10, pp. 2775–2782, 2022.
  • A. Abdoljalil, and M. Iri, “Brain tumor type classification using deep features of MRI images and optimized RBFNN,” ENG Trans., Vol. 2, pp. 1–7, 2021.
  • K. Swaraja, K. Meenakshi, H. B. Valiveti, and G. K. Karuna, “Segmentation and detection of brain tumor through optimal selection of integrated features using transfer learning,” Multimedia Tools and Applications., Vol. 81, no. 19, pp. 27363–27395, 2022.
  • T. Ruba, R. Tamilselvi, and M. P. Beham, “Brain tumor segmentation in multimodal MRI images using novel LSIS operator and deep learning,” J. Ambient. Intell. Humaniz. Comput., pp. 1–15, 2022.
  • M. Arif, F. Ajesh, S. Shamsudheen, O. Geman, D. Izdrui, and D. Vicoveanu, “Brain tumor detection and classification by MRI using biologically inspired orthogonal wavelet transform and deep learning techniques,” J. Healthc. Eng., Vol. 2022, pp. 1–18, 2022.
  • J. Amin, M. A. Anjum, M. Sharif, S. Jabeen, S. Kadry, and P. M. Ger, “A new model for brain tumor detection using ensemble transfer learning and quantum variational classifier,” Comput. Intell. Neurosci., Vol. 2022, pp. 1–13, 2022.
  • G. Sahar, S. Akbar, and H. U. Khan, “Automated detection of brain tumor through magnetic resonance images using convolutional neural network,” BioMed Res. Int., Vol. 2021, pp. 1–14, 2021.
  • A. K. Sharma, A. Nandal, A. Dhaka, D. Koundal, D. C. Bogatinoska, and H. Alyami, “Enhanced watershed segmentation algorithm-based modified ResNet50 model for brain tumor detection,” BioMed Res. Int., Vol. 2022, pp. 1–14, 2022.
  • S. Luo, L. Rongxin, and O. Sébastien, “A new deformable model using dynamic gradient vector flow and adaptive balloon forces,” in APRS Workshop on Digital Image Computing, Brisbane, Australia, 2003.
  • A. Rajendran, and R. Dhanasekaran, “Brain tumor segmentation on MRI brain images with fuzzy clustering and GVF snake model,” Int. J. Comput. Commun. Control, Vol. 7, no. 3, pp. 530–539, 2014.
  • M. A. Guerroudji, K. Amara, D. Aouam, N. Zenati, O. Djekoune, and M. Masmoudi, “Brain tumor segmentation on MRI using a GVF snake model,” in 7th International Conference on Image and Signal Processing and their Applications (ISPA), IEEE Access, 2022, pp. 1–5.
  • F. Özyurt, E. Sert, E. Avci, and E. Dogantekin, “Brain tumor detection based on convolutional neural network with neutrosophic expert maximum fuzzy sure entropy,” Measurement, Vol. 147, p. 106830, 2019.
  • D. L. Collins, A. P. Zijdenbos, V. Kollokian, J. G. Sled, N. J. Kabani, C. J. Holmes, and A. C. Evans, “Design and construction of a realistic digital brain phantom,” IEEE Trans. Med. Imaging, Vol. 17, no. 3, pp. 463–468, 1998.
  • P. Pietro, and M. Jitendra, “Scale-space and edge detection using anisotropic diffusion,” IEEE Trans. Pattern Anal. Mach. Intell., Vol. 12, no. 7, pp. 629–639, 1990.
  • A. Arya, V. Bhateja, M. Nigam, and A. S. Bhadauria, “Enhancement of brain MR-T1/T2 images using mathematical morphology,” in Information and Communication Technology for Sustainable Development, Singapore: Springer, 2020, pp. 833–840.
  • P. V. Ingole, and K. D. Kulat, “Morphological segmentation based fuzzy features for retrieval of brain MRI,” IETE. J. Res., Vol. 57, no. 4, pp. 331–345, 2011.
  • K. Michael, A. Witkin, and D. Terzopoulos, “Snakes: Active contour models,” Int. J. Comput. Vision, Vol. 1, no. 4, pp. 321–331, 1988.
  • N. Reddy Soora, E. U. Rahman Mohammed, S. Waseem Mohammed, and N. C. Santosh Kumar, “Deep active contour-based capsule network for medical image segmentation,” IETE. J. Res., 1–11, 2022.
  • M. F. Zarandi, M. Zarinbal, and M. Izadi, “Systematic image processing for diagnosing brain tumors: A Type-II fuzzy expert system approach,” Appl. Soft. Comput., Vol. 11, no. 1, pp. 285–294, 2011.
  • C. Xu, and J. L. Prince, “Gradient vector flow: A new external force for snakes,” in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, IEEE, 1997.
  • K. Machhale, B. Nandpuru, V. Kapur, and L. Kosta, “MRI brain cancer classification using hybrid classifier (SVM-KNN),” in Proceedings of International Conference on Industrial Instrumentation Control (ICIC), 2015, pp. 60–65.
  • W. Dou, M. Zhang, X. Zhang, X. Y. Li, H. Chen, and S. Li, “Convex-envelope based automated quantitative approach to multi-voxel 1H-MRS applied to brain tumor analysis,” PloS One, Vol. 10, no. 9, pp. e0137850, 2015.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.