Open Access
ITM Web Conf.
Volume 53, 2023
2nd International Conference on Data Science and Intelligent Applications (ICDSIA-2023)
Article Number 02008
Number of page(s) 14
Section Machine Learning / Deep Learning
Published online 01 June 2023
  1. Zailan, Z. N., Mostafa, S. A., Abdulmaged, A. I., Baharum, Z., Jaber, M. M., & Hidayat, R. 2022. Deep Learning Approach for Prediction of Brain Tumor from Small Number of MRI Images. JOIV: International Journal on Informatics Visualization, 6(2-2), 581-586. [CrossRef] [Google Scholar]
  2. Mohiyuddin, A., Basharat, A., Ghani, U., Abbas, S., Naeem, O. B., & Rizwan, M. 2022. Breast tumor detection and classification in mammogram images using modified YOLOv5 network. Computational and Mathematical Methods in Medicine, 2022. [Google Scholar]
  3. Amin, J., Anjum, M. A., Sharif, M., Jabeen, S., Kadry, S., & Moreno Ger, P. 2022. A New Model for Brain Tumor Detection Using Ensemble Transfer Learning and Quantum Variational Classifier. Computational Intelligence and Neuroscience, 2022. [Google Scholar]
  4. Kumar, T. S., Arun, C., & Ezhumalai, P. 2022. An approach for brain tumor detection using optimal feature selection and optimized deep belief network. Biomedical Signal Processing and Control, 73, 103440. [CrossRef] [Google Scholar]
  5. Alsaif, H., Guesmi, R., Alshammari, B. M., Hamrouni, T., Guesmi, T., Alzamil, A., & Belguesmi, L. 2022. A Novel Data Augmentation-Based Brain Tumor Detection Using Convolutional Neural Network. Applied Sciences, 12(8), 3773. [CrossRef] [Google Scholar]
  6. Qureshi, S. A., Raza, S. E. A., Hussain, L., Malibari, A. A., Nour, M. K., Rehman, A. U., ... & Hilal, A. M. 2022. Intelligent Ultra-Light Deep Learning Model for Multi-Class Brain Tumor Detection. Applied Sciences, 12(8), 3715. [CrossRef] [Google Scholar]
  7. Patil, R. B., Ansingkar, N., & Deshmukh, P. D. 2022. Deep Learning Based Brain Tumor Segmentation: Recent Updates. Rising Threats in Expert Applications and Solutions, 395-405. [CrossRef] [Google Scholar]
  8. Rathod, R., & Khan, R. A. H. 2021. Brain Tumor Detection Using Deep Neural Network and Machine Learning Algorithm. PalArch’s Journal of Archaeology of Egypt/Egyptology, 18(08), 1085-1093. [Google Scholar]
  9. Nazir, M., Shakil, S., & Khurshid, K. 2021. Role of deep learning in brain tumor detection and classification (2015 to 2020): A review. Computerized Medical Imaging and Graphics, 91, 101940. [CrossRef] [Google Scholar]
  10. Amin, J., Sharif, M., Gul, N., Raza, M., Anjum, M. A., Nisar, M. W., & Bukhari, S. A. C. 2020. Brain tumor detection by using stacked autoencoders in deep learning. Journal of medical systems, 44(2), 1-12. [CrossRef] [Google Scholar]
  11. Sadad, T., Rehman, A., Munir, A., Saba, T., Tariq, U., Ayesha, N., & Abbasi, R. 2021. Brain tumor detection and multi‐classification using advanced deep learning techniques. Microscopy Research and Technique, 84(6), 1296-1308. [CrossRef] [Google Scholar]
  12. Saba, T., Mohamed, A. S., El-Affendi, M., Amin, J., & Sharif, M. 2020. Brain tumor detection using fusion of hand crafted and deep learning features. Cognitive Systems Research, 59, 221-230. [CrossRef] [Google Scholar]
  13. Nanware, D., Taras, S., & Navale, S. 2020. Brain Tumor Detection Using Deep Learning. Int. J. Sci. Res. Eng. Dev, 3, 391-395. [Google Scholar]
  14. Satpute, B. S., Kale, A., Dhande, D., Kuber, H., & Chore, S. 2020. Brain Tumor Detection using Deep Learning Technique. [Google Scholar]
  15. Javed, A. R., Sarwar, M. U., Beg, M. O., Asim, M., Baker, T., and Tawfik, H. 2020. A collaborative healthcare framework for shared healthcare plan with ambient intelligence. sHuman Centric Comput. Inf. Sci. 10, 1–21. [Google Scholar]
  16. Zhou, H., Hu, R., Tang, O., Hu, C., Tang, L., Chang, K., ... & Zhu, C. 2020. Automatic machine learning to differentiate pediatric posterior fossa tumors on routine MR imaging. American Journal of Neuroradiology, 41(7), 1279-1285. [CrossRef] [MathSciNet] [Google Scholar]
  17. Kabir, M. A. (2020). Automatic brain tumor detection and feature extraction from MRI image. GSJ, 8(4). [Google Scholar]
  18. Mudda, M., Manjunath, R., & Krishnamurthy, N. 2020. Brain tumor classification using enhanced statistical texture features. IETE Journal of Research, 1-12. [Google Scholar]
  19. Swapnil, S. A., & Girish, V. S. 2020, March. Image mining methodology for detection of brain tumor: a review. In 2020 Fourth International Conference on Computing Methodologies and Communication (ICCMC) (pp. 232-237). IEEE. [Google Scholar]
  20. Sharif, M., Amin, J., Raza, M., Yasmin, M., & Satapathy, S. C. 2020. An integrated design of particle swarm optimization (PSO) with fusion of features for detection of brain tumor. Pattern Recognition Letters, 129, 150-157. [CrossRef] [Google Scholar]
  21. Siar, M., & Teshnehlab, M. 2019, October. Brain tumor detection using deep neural network and machine learning algorithm. In 2019 9th international conference on computer and knowledge engineering (ICCKE) (pp. 363-368). IEEE. [Google Scholar]
  22. Maheswari, G., & Shaheena, K. V. 2017. MRI Brain Tumor Segmentation Algorithms and Approaches-A Survey. International Journal of Latest Engineering and Management Research (IJLEMR), 2(10), 33-37. [Google Scholar]
  23. Beers, A., Chang, K., Brown, J., Sartor, E., Mammen, C. P., Gerstner, E., ... & KalpathyCramer, J. 2017. Sequential 3d u-nets for biologically-informed brain tumor segmentation. arXiv preprint arXiv:1709.02967. [Google Scholar]
  24. Kaur, H., & Gill, A. K. 2017. Review of Brain Tumor Detection Using Various Techniques. Int. J. Adv. Res. Comput. Sci. Softw. Eng., 7(5), 867-870. [CrossRef] [Google Scholar]
  25. Kapoor, L., & Thakur, S. 2017. A survey on brain tumor detection using image processing techniques. In 2017 7th international conference on cloud computing, data science & engineering-confluence (pp. 582-585). IEEE. [Google Scholar]
  26. Kamnitsas, K., Ledig, C., Newcombe, V. F., Simpson, J. P., Kane, A. D., Menon, D. K., ... & Glocker, B. (2017). Efficient multi-scale 3D CNN with fully connected CRF for accurate brain lesion segmentation. Medical image analysis, 36, 61-78. [CrossRef] [Google Scholar]
  27. Anitha, V., & Murugavalli, S. J. I. C. V. 2016. Brain tumour classification using two‐tier classifier with adaptive segmentation technique. IET computer vision, 10(1), 9-17. [Google Scholar]
  28. Goodfellow, Y., Bengio, A., & Courville, D. L. 2016. MIT Press: Cambridge. MA, USA. [Google Scholar]
  29. Kapse, R. S., Salankar, S. S., & Babar, M. 2015. Literature survey on detection of brain tumor from MRI images. IOSR Journal of Electronics and Communication Engineering (IOSR-JECE), 10(1), 80-86. [Google Scholar]
  30. Sindhu, A., & Meera, S. 2015. A survey on detecting brain tumorinmri images using image processing techniques. International Journal of Innovative Research in Computer and Communication Engineering, 3(1), 16. [Google Scholar]
  31. Agrawal, S., & Xaxa, D. K. 2014. Survey on image segmentation techniques and color models. International Journal of Computer Science and Information Technologies, 5(3), 3025-3030. [Google Scholar]
  32. Urban, G., Bendszus, M., Hamprecht, F., & Kleesiek, J. 2014. Multi-modal brain tumor segmentation using deep convolutional neural networks. MICCAI BraTS (brain tumor segmentation) challenge. Proceedings, winning contribution, 31-35. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.