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Automatika
Journal for Control, Measurement, Electronics, Computing and Communications
Volume 65, 2024 - Issue 3
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Regular Paper

Ensemble 3D CNN and U-Net-based brain tumour classification with MKKMC segmentation

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Pages 691-705 | Received 08 Nov 2023, Accepted 02 Feb 2024, Published online: 15 Feb 2024

References

  • Seetha J, Raja SS. Brain tumor classification using convolutional neural networks. Biomed Pharmacol J. 2018;11(3):1457.
  • Deepak S, Ameer PM. Brain tumor classification using deep CNN features via transfer learning. Comput Biol Med. 2019;111:103345.
  • Muhammad K, Khan S, Del Ser J, et al. Deep learning for multigrade brain tumor classification in smart healthcare systems: A prospective survey. IEEE Trans Neural Networks Learn Syst. 2020;32(2):507–522.
  • Ghassemi N, Shoeibi A, Rouhani M. Deep neural network with generative adversarial networks pre-training for brain tumor classification based on MR images. Biomed Signal Process Control. 2020;57:101678.
  • Kaplan K, Kaya Y, Kuncan M, et al. Brain tumor classification using modified local binary patterns (LBP) feature extraction methods. Med Hypotheses. 2020;139:109696.
  • Alqudah AM, Alquraan H, Qasmieh IA, et al. (2020). Brain tumor classification using deep learning technique–a comparison between cropped, uncropped, and segmented lesion images with different sizes. arXiv preprint arXiv:2001.08844.
  • Ge C, Gu IYH, Jakola AS, et al. Enlarged training dataset by pairwise gans for molecular-based brain tumor classification. IEEE Access. 2020;8:22560–22570.
  • Sultan HH, Salem NM, Al-Atabany W. Multi-classification of brain tumor images using deep neural network. IEEE Access. 2019;7:69215–69225.
  • Kaur T, Saini BS, Gupta S. An optimal spectroscopic feature fusion strategy for MR brain tumor classification using Fisher criteria and parameter-free BAT optimization algorithm. Biocybern Biomed Eng. 2018;38(2):409–424.
  • Shree NV, Kumar TNR. Identification and classification of brain tumor MRI images with feature extraction using DWT and probabilistic neural network. Brain Inform. 2018;5(1):23–30.
  • Raju AR, Suresh P, Rao RR. Bayesian HCS-based multi-SVNN: a classification approach for brain tumor segmentation and classification using Bayesian fuzzy clustering. Biocybern Biomed Eng. 2018;38(3):646–660.
  • Pugalenthi R, Rajakumar MP, Ramya J, et al. Evaluation and classification of the brain tumor MRI using machine learning technique. J Control Eng Appl Inform. 2019;21(4):12–21.
  • Tahir B, Iqbal S, Usman Ghani Khan M, et al. Feature enhancement framework for brain tumor segmentation and classification. Microsc Res Tech. 2019;82(6):803–811.
  • Anaraki AK, Ayati M, Kazemi F. Magnetic resonance imaging-based brain tumor grades classification and grading via convolutional neural networks and genetic algorithms. Biocybern Biomed Eng. 2019;39(1):63–74.
  • Narmatha C, Eljack SM, Tuka AARM, et al. A hybrid fuzzy brain-storm optimization algorithm for the classification of brain tumor MRI images. J Ambient Intell Humaniz Comput. 2020;11(8):1–9.
  • Sharif M, Tanvir U, Munir EU, et al. Brain tumor segmentation and classification by improved binomial thresholding and multi-features selection. J Ambient Intell Humaniz Comput. 2022;8(4):3161–3183.
  • Khan MA, Lali IU, Rehman A, et al. Brain tumor detection and classification: a framework of marker-based watershed algorithm and multilevel priority features selection. Microsc Res Tech. 2019;82(6):909–922.
  • Ajai AR, Gopalan S. Analysis of active contours without edge-based segmentation technique for brain tumor classification using SVM and KNN classifiers. In: Advances in communication systems and networks. Singapore: Springer; 2020;65(6):1–10.
  • Sahoo L, Sarangi L, Dash BR, et al. Detection and classification of brain tumor using magnetic resonance images. In: Advances in electrical control and signal systems. Singapore: Springer; 2020;665:429–441.
  • David DS, Jayachandran A. Robust classification of brain tumor in MRI images using salient structure descriptor and RBF kernel-SVM. TAGA J Graphic Technol. 2018;14(64):718–737.
  • Swati ZNK, Zhao Q, Kabir M, et al. Brain tumor classification for MR images using transfer learning and fine-tuning. Comput Med Imaging Graph. 2019;75:34–46.
  • Amin J, Sharif M, Gul N, et al. Brain tumor classification based on DWT fusion of MRI sequences using convolutional neural network. Pattern Recognit Lett. 2020;129:115–122.
  • El-Mahelawi JK, Abu-Daqah JU, Abu-Latifa RI, et al. Tumor classification using artificial neural networks. Int J Acad Eng Res (IJAER). 2020;4(11):8–15.
  • Sajid S, Hussain S, Sarwar A. Brain tumor detection and segmentation in MR images using deep learning. Arab J Sci Eng. 2019;44(11):9249–9261.
  • Saouli R, Akil M, Kachouri R. Fully automatic brain tumor segmentation using end-to-end incremental deep neural networks in MRI images. Comput Methods Programs Biomed. 2018;166:39–49.
  • Naz ARS, Naseem U, Razzak I, et al. Deep autoencoder-decoder framework for semantic segmentation of brain tumor. Aust J Intell Inf Process Syst. 2019;15(4):53–60.
  • Noreen N, Palaniappan S, Qayyum A, et al. A deep learning model based on concatenation approach for the diagnosis of brain tumor. IEEE Access. 2020;8:55135–55144.