پیوندهای مفید
آمار
| تعداد نشریات | 22 |
| تعداد شمارهها | 709 |
| تعداد مقالات | 10,213 |
| تعداد مشاهده مقاله | 71,680,424 |
| تعداد دریافت فایل اصل مقاله | 63,496,258 |
An efficient framework for glioma tumor classifications and diagnosis using proposed CNN architecture | ||
| International Journal of Nonlinear Analysis and Applications | ||
| مقاله 127، دوره 13، شماره 2، مهر 2022، صفحه 1577-1584 اصل مقاله (397.48 K) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22075/ijnaa.2021.24406.2733 | ||
| نویسندگان | ||
| Jeevanantham V* 1؛ Premkumar M2؛ Ashokkumar S. R.2؛ Anupallavi S3؛ Dhamodharan S1 | ||
| 1Department of ECE, SSM Institute of Engineering and Technology, Dindigul, India | ||
| 2Department of ECE, Sri Eshwar College of Engineering and Technology, Coimbatore, India | ||
| 3Department of ECE, VSB College of Engineering Technical Campus, Coimbatore, India | ||
| تاریخ دریافت: 09 مرداد 1400، تاریخ بازنگری: 12 شهریور 1400، تاریخ پذیرش: 19 شهریور 1400 | ||
| چکیده | ||
| This article proposes the deep learning algorithm- Convolutional Neural Networks (CNN) for both Glioma tumor classifications and diagnosis process. This proposed CNN architecture is derived from the conventional CNN architecture to obtain the optimum classification and diagnosis accuracy. This proposed CNN architecture is derived from the conventional system for obtaining the high classification and diagnosis performance. This proposed methodology stated in this paper uses BRATS 2015 open access dataset for obtaining the brain Magnetic Resonance Image (MRI) for tumor region detection. The proposed methodology stated in this paper for tumor diagnosis achieves 97.7% of Jaccard Index (J) and 83.8% of Dice Similarity Index (DSI) and 99.025 of Diagnosis Rate (DR) using CNN algorithm. | ||
| کلیدواژهها | ||
| Glioma؛ tumor؛ deep learning algorithm؛ classification؛ diagnosis | ||
| مراجع | ||
|
[1] A. Bousselham, O. Bouattane, M. Youssfi and A. Raihani, Towards reinforced brain tumor segmentation on MRI images based on temperature changes on pathologic area, Int. J. Biomed. Imag. 2019 (2019), Article ID 1758948, 1–18. [2] BRATS database, (2015). http://braintumorsegmentation.org/ [3] H. Greenspan, A. Ruf and J. Goldberger, Constrained Gaussian mixture model framework for automatic segmentation of MR brain images, IEEE Trans. Med. Imag. 25 (2006), no. 9, 1233–1245. [4] N. Gordillo, E. Montseny and P. Sobrevilla, State of the art survey on MRI brain tumor segmentation, Magnetic Resonance Imag. 31 (2013), no. 8, 1426–1438. [5] Z. Ji, Y. Xia, Q. Sun, Q. Chen and D. Feng, Adaptive scale fuzzy local Gaussian mixture model for brain mr image segmentation, Neurocomput. 134 (2014), no. 12, 60—69. [6] P. John, Brain tumor classification using wavelet and texture based neural network, Int. J. Sci. Engin. Res. 3 (2012), no. 10, 1–7. [7] K. Kalti and M.A. Mahjoub, Image segmentation by Gaussian mixture models and modified FCM algorithm, Int. Arab J. Inf. Technol. 11 (2014), no. 1, 11—18. [8] P. Kumar and B. VijayKumar, Brain tumor MRI segmentation and classification using ensemble classifier, Int. J. Recent Technol. Engin. 8 (2019), no. 1S4. [9] G.C. Lin, W.J. Wang, C.C. Kang and C.M. Wang, Multispectral MR images segmentation based on fuzzy knowledge and modified seeded region growing, Magnetic Resonance Imag. 30 (2012), no. 2, 230—246. [10] I. Maiti and M. Chakraborty, A new method for brain tumor segmentation based on watershed and edge detection algorithms in hsv colour model, IEEE Nat. Conf. Comput. Commun. Syst. (NCCCS), 2012, pp. 1—5. [11] N. Mathur, S. Mathur and D. Mathur, A novel approach to improve Sobel edge detector, Procedia Comput. Sci.93 (2016), no. 16, 431—438. [12] P.A. Mei, C. de Carvalho Carneiro, S.J. Fraser, L.L. Min and F. Reis, Analysis of neoplastic lesions in magnetic resonance imaging using self-organizing maps, J. Neurol. Sci. 359 (2015), no. 1, 78-–83. [13] K. Nimeesha and R.M. Gowda, Brain tumour segmentation using k-means and fuzzy c-means clustering algorithm, Int. J. Comput. Sci. Inf. Technol. Res. Excell. 3 (2013), no. 2, 60—65. [14] T.U. Paul and S.K. Bandhyopadhyay, Segmentation of brain tumor from brain mri images reintroducing k–means with advanced dual localization method, Int. J. Engin. Res. Appl. 2 (2012), no. 3, 226-–231. [15] K.A. Viji and J. JayaKumari, Modified texture based region growing segmentation of MR brain images, IEEE Conf. Inf. Commun. Technol. (ICT), 2013, pp. 691—695. [16] A. Vishnuvarthanan, M.P. Rajasekaran, V. Govindaraj, Y. Zhang and A. Thiyagarajan, Development of a combinational framework to concurrently perform tissue segmentation and tumor identification in T1-w, T2-w, Flair and MPR type magnetic resonance brain images, Expert Syst. Appl. 95 (2018), no. 15, 280–311. [17] A. Zotin, K. Simonov, M. Kurako, Y. Hamad and S. Kirillova, Edge detection in mri brain tumor images based on fuzzy c-means clustering, Procedia Comput. Sci. 126 (2018), 1261—1270. | ||
|
آمار تعداد مشاهده مقاله: 44,988 تعداد دریافت فایل اصل مقاله: 50,971 |
||