Open Access
Issue |
ITM Web Conf.
Volume 52, 2023
International Conference on Connected Object and Artificial Intelligence (COCIA’2023)
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Article Number | 02005 | |
Number of page(s) | 12 | |
Section | Artificial Intelligence and its Application | |
DOI | https://doi.org/10.1051/itmconf/20235202005 | |
Published online | 08 May 2023 |
- Z. Liu, L. Tong, L. Chen, Z. Jiang, F. Zhou, Q. Zhang, X. Zhang, Y. Jin, H. Zhou. Deep learning based brain tumor segmentation: a survey. https://doi.org/10.1007/s40747-022-00815-5 [Google Scholar]
- M. Moreno Lopez, J. Ventura. Dilated convolutions for brain tumor segmentation in mri scans. (2017). International MICCAI Brain Lesion Workshop, pp. 253–262. Springer [Google Scholar]
- R. Sharma, Aashima, M, Nanda, C. Fronterre, P. Sewagudde, E. Anna, S Sentongo. Yenney, K. D. Arhin, N. Oh, J. Amponsah-Manu, F. Ssentongo, P. Mapping Cancer in Africa: A Comprehensive and Comparable Characterization of 34 Cancer Types Using Estimates From GLOBOCAN 2020, P 5. doi: 10.3389/fpubh.2022.839835 [Google Scholar]
- J.S. Thanga Purni, R. Vedhapriyavadhana, S. L. Jayalakshmi, R. Girija. High Performance Classifier for Brain Tumor Detection Using Capsule Neural Network Computer Vision and Machine Intelligence Paradigms for SDGs, Lecture Notes in Electrical Engineering 967, (2023) pp (152-164). https://doi.org/10.1007/978-981-19-7169-3_14 [Google Scholar]
- Fluid Attenuation Inversion Recovery. Radiopaedia.org. Available online: https://radiopaedia.org/articles/fluid-attenuated-inversion-recovery (accessed on 20 january 2023). [Google Scholar]
- https://www.brainlesion-workshop.org/ [Google Scholar]
- http://braintumorsegmentation.org/ [Google Scholar]
- B.H. Menze; A. Jakab; S. Bauer; J. Kalpathy-Cramer; K. Farahani; J. Kirby; Y. Burren; N. Porz; J. Slotboom; R. Wiest; L. Lanczi; E. Gerstner; M. Weber; T. Arbel; B.B. Avants; N. Ayache; P. Buendia; D.L. Collins; N. Cordier; J.J. Corso; A. Criminisi; T. Das; H. Delingette; Ç. Demiralp; C.R. Durst; M. Dojat; S. Doyle; J. Festa; F. Forbes; E. Geremia; B. Glocker; P. Golland; X. Guo; A. Hamamci; K.M. Iftekharuddin; R. Jena; N.M. John; E. Konukoglu; D. Lashkari; J.A. Mariz; R. Meier; S. Pereira; D. Precup; S.J. Price; T.R. Raviv; S.M.S. Reza; M. Ryan; D. Sarikaya; L. Schwartz; H. Shin; J. Shotton; C.A. Silva; N. Sousa; N.K. Subbanna; G. Szekely; T.J. Taylor; O.M. Thomas; N.J. Tustison; G. Unal; F. Vasseur; M. Wintermark; D.H. Ye; L. Zhao; B. Zhao; D. Zikic; M. Prastawa; M. Reyes; K.V. Leemput. The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS). IEEE TRANSACTIONS ON MEDICAL IMAGING, VOL. 34, NO. 10, OCTOBER 2015. DOI:10.1109/TMI.2014.2377694 [Google Scholar]
- https://www.oasis-brains.org. (2023). OASIS Brains Open Access Series of Imaging Studies. [online] Available at: http://www.oasis-brains.org/#data [Accessed 28 january 2023] [Google Scholar]
- Diamant, A.; Chatterjee, A.; Vallières, M.; Shenouda, G.; Seuntjens, J. Deep learning in head & neck cancer outcome prediction. Sci. Rep. 2019, 9, 2764. [CrossRef] [Google Scholar]
- AlBadawy, E.A.; Saha, A.; Mazurowski, M.A. Deep learning for segmentation of brain tumors: Impact of cross-institutional training and testing. Med. Phys. 2018, 45, 1150–1158. [CrossRef] [Google Scholar]
- Guerrero, R.; Qin, C.; Oktay, O.; Bowles, C.; Chen, L.; Joules, R.; Wolz, R.; Valdés-Hernández, M.D.; Dickie, D.A.; Wardlaw, J.; et al. White matter hyperintensity and stroke lesion segmentation and differentiation using convolutional neural networks. Neuroimage. Clin. 2018, 43, 929–939. [Google Scholar]
- Kamnitsas, K.; Ledig, C.; Newcombe, V.F.J.; Simpson, J.P.; Kane, A.D.; Menon, D.K.; Rueckert, D.; Glocker, B. Efficient multi-scale3D CNN with fully connected CRF for accurate brain lesion segmentation. Med. Image Anal. 2017, 36, 61–78. [CrossRef] [Google Scholar]
- Basheera, S.; Ram, M.S.S. Classification of Brain Tumors Using Deep Features Extracted Using CNN. J. Phys. Conf. Ser. 2019, 1172, 012016. [CrossRef] [Google Scholar]
- Pollak Dorocic, I.; Fürth, D.; Xuan, Y.; Johansson, Y.; Pozzi, L.; Silberberg, G.; Carlén, M.; Meletis, K. A Whole-Brain Atlas of Inputs to Serotonergic Neurons of the Dorsal and Median Raphe Nuclei. Neuron 2014, 83, 663–678. [CrossRef] [Google Scholar]
- Pereira, S.; Pinto, A.; Oliveira, J.; Mendrik, A.M.; Correia, J.H.; Silva, C.A. Automatic brain tissue segmentation in MR images using Random Forests and Conditional Random Fields. J. Neurosci. Methods 2016, 270, 111–123. [CrossRef] [Google Scholar]
- Shakeri, M.; Tsogkas, S.; Ferrante, E.; Lippe, S.; Kadoury, S.; Paragios, N.; Kokkinos, I. Sub-cortical brain structure segmentation using F-CNN’S. In Proceedings of the 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), Prague, Czech Republic, 13–16 April 2016; pp. 269–272. [Google Scholar]
- Kleesiek, J.; Urban, G.; Hubert, A.; Schwarz, D.; Maier-Hein, K.; Bendszus, M.; Biller, A. Deep MRI brain extraction: A 3D convolutional neural network for skull stripping. NeuroImage 2016, 129, 460–469. [CrossRef] [Google Scholar]
- Jurek, J.; Kociński, M.; Materka, A.; Elgalal, M.; Majos, A. CNN-based superresolution reconstruction of 3D MR images using thick-slice scans. Biocybern. Biomed. Eng. 2020, 40, 111–125. [Google Scholar]
- Grimm, F.; Edl, F.; Kerscher, S.R.; Nieselt, K.; Gugel, I.; Schuhmann, M.U. Semantic segmentation of cerebrospinal fluid and brain volume with a convolutional neural network in pediatric hydrocephalus—Transfer learning from existing algorithms. Acta Neurochir. 2020, 162, 2463–2474. [CrossRef] [Google Scholar]
- Kalpathy-Cramer, J.; Freymann, J.B.; Kirby, J.S.; Kinahan, P.E.; Prior, F.W. Quantitative Imaging Network: Data Sharing and Competitive Algorithm Validation Leveraging The Cancer Imaging Archive. Transl. Oncol. 2014, 1, 147–152. [CrossRef] [Google Scholar]
- Ahmed M. Gab Allah, Amany M. Sarhan, Nada M. Elshennawy. Edge U-Net: Brain tumor segmentation using MRI based on deep U-Net model with boundary information. [Google Scholar]
- R. Raza; U.L. Bajwa; Y. Mehmood; M.W. Anwar; M.H. Jamal. dResU-Net: 3D deep residual U-Net based brain tumor segmentation from multimodal MRI. [Google Scholar]
- X.L. Gongning; K. Wang. Multi-step Cascaded Networks for Brain Tumor Segmentation. School of Computer Science and Technology, Harbin Institute of Technology, Harbin 150001, China. arXiv:1908.05887v3 [eess.IV] 25 Sep 2019 [Google Scholar]
- Shujing Li; Linguo Li. DRT-Unet: A Segmentation Network for Aiding Brain Tumor Diagnosis. (2022),. https://doi.org/10.1155/2022/2546466 [Google Scholar]
- https://www5.cs.fau.de/research/data/fundus-images/ [Google Scholar]
- Vimal Kurup R, V. Sowmya and K. P. Soman. Effect of Data Pre-Processing on Brain Tumor Classification Using Capsulenet. researchgate. [Google Scholar]
- S, Saman. S, Jamjala Narayanan. Survey on brain tumor segmentation and feature extraction of MR images. Springer Nature 2018.https://doi.org/10.1007/s13735-018-0162-2 [Google Scholar]
- Md.A Siddique. S. Sakib. M.M. Rahman Khan. Deep Convolutional Neural Networks Model-based Brain Tumor Detection in Brain MRI Images [Google Scholar]
- M. Pendse, V. Thangarasa, V. Chiley, R. Holmdahl, J. Hestness, D. DeCoste. Memory Efficient 3D U-Net with Reversible Mobile Inverted Bottlenecks for Brain Tumor Segmentation. ArXiv:2104.099648v2 [eess.IV] 21 Apr 2021. [Google Scholar]
- A. Gumaei, M.M Hassan, MD.R. Hassan, A. Alelaiwi, G. Fortin. A Hybrid Feature Extraction Method With Regularized Extreme Learning Machine for Brain Tumor Classification [Google Scholar]
- J. Cheng. ‘‘Brain tumor dataset (version 5), ’’ 2017. doi: 10.6084/m9.figshare.1512427.v5. [Google Scholar]
- J.S. Thanga Purni, R. Vedhapriyavadhana, S. L. Jayalakshmi, R. Girija. High Performance Classifier for Brain Tumor Detection Using Capsule Neural Network. Lecture Notes in Electrical Engineering 967. https://doi.org/10.1007/978-981-19-7169-3_14 [Google Scholar]
- H. Mehnatkesh, S.M. Jafar Jalali, A. Khosravi, S. Nahavandi. An intelligent driven deep residual learning framework for brain tumor classification using MRI images. https://doi.org/10.1016/j.eswa.2022.119087 [Google Scholar]
- Techa, C., Ridouani, M., Hassouni, L., & Anoun, H. (2022, November). Alzheimer’s Disease Multi-class Classification Model Based on CNN and StackNet Using Brain MRI Data. In Proceedings of the 8th International Conference on Advanced Intelligent Systems and Informatics 2022 (pp. 248-259). Cham: Springer International Publishing. [Google Scholar]
- Rabbah, J., Ridouani, M., Hassouni, L.: A New Classification Model Based on Stacknet and Deep Learning for Fast Detection of COVID 19 Through X Rays Images. In: Fourth International Conference on Intelligent Computing in Data Sciences (ICDS), pp. 1-8. (2020). [Google Scholar]
- Elaanba, Abdelfettah, Mohammed Ridouani, and Larbi Hassouni. “A Stacked Generalization Chest-X-Ray-Based Framework for Mispositioned Medical Tubes and Catheters Detection.” Biomedical Signal Processing and Control 79 (2023): 104111.” [CrossRef] [Google Scholar]
- Elaanba A, Ridouani M, Hassouni L. Automatic detection using deep convolutional neural networks for 11 abnormal positioning of tubes and catheters in chest x-ray images. In2021 IEEE World AI IoT Congress (AIIoT) 2021 May 10 (pp. 0007-0012). IEEE. [Google Scholar]
- Rabbah, J., Ridouani, M., Hassouni, L.: A New Churn Prediction Model Based on Deep Insight Features Transformation for Convolution Neural Network Architecture and Stacknet. (IJWLTT), 17(1), 1-18. (2022). [Google Scholar]
- Benazzouza, S., Ridouani, M., Salahdine, F., Hayar, A.: A Novel Prediction Model for Malicious Users Detection and Spectrum Sensing Based on Stacking and Deep Learning. Sensors 22 (17), 6477 (2022). [CrossRef] [Google Scholar]
- N. Siddique, S. Paheding, C.P. Elkin, V. Devabhaktuni. U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications. (2021). Digital Object Identifier 10.1109/ACCESS.2021.308602 [Google Scholar]
- Zikic D, Ioannou Y, Brown M, Criminisi A (2014) Segmentation of brain tumor tissues with convolutional neural networks. Proceedings MICCAI-BRATS 36:36–39 [Google Scholar]
- Havaei M, Davy A, Warde-Farley D, Biard A, Courville A, Bengio Y, Pal C, Jodoin PM, Larochelle H (2017) Brain tumor segmentation with deep neural networks. Med Imag Anal 35:18–31 [CrossRef] [Google Scholar]
- Ereira S, Pinto A, Alves V, Silva CA (2016) Brain tumor segmentation using convolutional neural networks in mri images. IEEE Trans Comput Imag 35(5):1240–1251 [CrossRef] [Google Scholar]
- Long J, Shelhamer E, Darrell T (2015) Fully convolutional networks for semantic segmentation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 3431–3440 [Google Scholar]
- O. Ronneberger ;P. Fischer. Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention, pp. 234–241. Springer [Google Scholar]
- k. Muhammed, S. Khan, J.D. Ser, C. Victor Hugo. Deep Learning for Multigrade Brain Tumor Classification in Smart Healthcare Systems: A Prospective Survey. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, VOL. 32, NO. 2, FEBRUARY 2021. DOI: 10.1109/TNNLS.2020.2995800 [Google Scholar]
- J. Xu, H. Liu, W. Shao, and K. Deng, “Quantitative 3-D shape features based tumor identification in the fog computing architecture, ” J. Ambient Intell. Humanized Comput., vol. 10, no. 8, pp. 2987–2997, Aug. 2019. [CrossRef] [Google Scholar]
- P. Costa et al., “End-to-end adversarial retinal image synthesis, ” IEEE Trans. Med. Imag., vol. 37, no. 3, pp. 781–791, Mar. 2018. [CrossRef] [Google Scholar]
- M. Mardani et al., “Deep generative adversarial neural networks for compressive sensing MRI, ” IEEE Trans. Med. Imag., vol. 38, no. 1, pp. 167–179, Jan. 2019 [CrossRef] [Google Scholar]
- A. Lucas, S. Lopez-Tapia, R. Molina, and A. K. Katsaggelos, “Gen-erative adversarial networks and perceptual losses for video super-resolution, ” IEEE Trans. Image Process., vol. 28, no. 7, pp. 3312–3327, Jul. 2019. [CrossRef] [MathSciNet] [Google Scholar]
- https://gco.iarc.fr/today/data/factsheets/populations/504-morocco-fact-sheets.pdf [Google Scholar]
- Sabour, S., Frosst, N., Hinton, G.E., 2017. Dynamic routing between capsules, in: Advances in Neural Information Processing Systems, pp. 3856–3866. [Google Scholar]
- Hinton, G.E., Sabour, S., Frosst, N., 2018. Matrix capsules with EM routing, in: International Conference on Learning Representations. [Google Scholar]
- Kosiorek, A., Sabour, S., Teh, Y.W., Hinton, G.E., 2019. Stacked capsule autoencoders, in: Advances in Neural Information Processing Systems, pp. 15512–15522. [Google Scholar]
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