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Title: :  Brain Tumor Detection and Classification using Neural Network
PaperId: :  14085
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 7 Issue 2 2021
DUI:    16.0415/IJARIIE-14085
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author NameAuthor Institute
Benila.YMadras Institute of Technology
V.VijayalakshmiMadras Institute of Technology

Abstract

Electronics Engineering
Brain Tumor, MRI, Segmentation, Deep learning, Convolutional neural network (CNN).
Brain tumor is one of the most dangerous disease which requires early diagnosis and accurate detection method. Tumor is the mass growth of abnormal cells. The tumor may be cancerous or non-cancerous, the cancerous cells are life threatening hence early diagnosis is necessary. The tumor can be detected by medical imaging techniques like Magnetic Resonance Imaging (MRI), which is most preferred imaging technique. The accurate detection of tumor in the MRI scan is quite challenging, hence segmentation is performed to detect the tumor accurately. The segmentation is done by clustering technique and then classification is performed. With the advances of deep learning in medical field the automatic classification of brain tumor can be performed. The convolutional neural network is used to perform classification of the tumor as benign or malignant. The convolutional neural network outperforms other machine learning algorithms and provides better results.

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IJARIIE Benila.Y, and V.Vijayalakshmi. "Brain Tumor Detection and Classification using Neural Network" International Journal Of Advance Research And Innovative Ideas In Education Volume 7 Issue 2 2021 Page 1562-1568
MLA Benila.Y, and V.Vijayalakshmi. "Brain Tumor Detection and Classification using Neural Network." International Journal Of Advance Research And Innovative Ideas In Education 7.2(2021) : 1562-1568.
APA Benila.Y, & V.Vijayalakshmi. (2021). Brain Tumor Detection and Classification using Neural Network. International Journal Of Advance Research And Innovative Ideas In Education, 7(2), 1562-1568.
Chicago Benila.Y, and V.Vijayalakshmi. "Brain Tumor Detection and Classification using Neural Network." International Journal Of Advance Research And Innovative Ideas In Education 7, no. 2 (2021) : 1562-1568.
Oxford Benila.Y, and V.Vijayalakshmi. 'Brain Tumor Detection and Classification using Neural Network', International Journal Of Advance Research And Innovative Ideas In Education, vol. 7, no. 2, 2021, p. 1562-1568. Available from IJARIIE, https://ijariie.com/AdminUploadPdf/Brain_Tumor__Detection__and_Classification_using_Neural_Network_ijariie14085.pdf (Accessed : ).
Harvard Benila.Y, and V.Vijayalakshmi. (2021) 'Brain Tumor Detection and Classification using Neural Network', International Journal Of Advance Research And Innovative Ideas In Education, 7(2), pp. 1562-1568IJARIIE [Online]. Available at: https://ijariie.com/AdminUploadPdf/Brain_Tumor__Detection__and_Classification_using_Neural_Network_ijariie14085.pdf (Accessed : )
IEEE Benila.Y, and V.Vijayalakshmi, "Brain Tumor Detection and Classification using Neural Network," International Journal Of Advance Research And Innovative Ideas In Education, vol. 7, no. 2, pp. 1562-1568, Mar-App 2021. [Online]. Available: https://ijariie.com/AdminUploadPdf/Brain_Tumor__Detection__and_Classification_using_Neural_Network_ijariie14085.pdf [Accessed : ].
Turabian Benila.Y, and V.Vijayalakshmi. "Brain Tumor Detection and Classification using Neural Network." International Journal Of Advance Research And Innovative Ideas In Education [Online]. volume 7 number 2 ().
Vancouver Benila.Y, and V.Vijayalakshmi. Brain Tumor Detection and Classification using Neural Network. International Journal Of Advance Research And Innovative Ideas In Education [Internet]. 2021 [Cited : ]; 7(2) : 1562-1568. Available from: https://ijariie.com/AdminUploadPdf/Brain_Tumor__Detection__and_Classification_using_Neural_Network_ijariie14085.pdf
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