Comparative Analysis of SVM and CNN Techniques for Brain Tumor Detection

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Dinesh M. Barode
Rupali S. Awhad
Vijay D. Dhangar
Dr. Seema S. Kawathekar

Abstract

A brain tumor is the most common disease on earth and it is harmful to people. Tumors are the uncontrolled growth of cells and tissues in the human brain called a tumor. The image is acquired using CT scans and Magnetic Resonance Images. The identification of tumors at an early stage is critical and challenging for researchers. A patient comes to the hospital when he starts suffering from pain, headache, omission etc and at that time, if he has a tumor, To recognize the tumor early stage it is very different to identify whether it is benign (non-cancerous) or malignant (cancerous), many techniques or methods are available for detection of tumor here we apply SVM algorithm and CNN on brain Magnetic Resonance Images for classification of a benign or malignant tumor. Here, we propose a system based on the new concept of simple tumor detection that uses feature extraction techniques, segmentation algorithm and classification. To identify similar patients who have or do not have a brain tumor, as well as to ascertain the type of tumor they have and their tumor sizes. By comparing both SVM & CNN which technique is more beneficial and which one is better in both? The performance of SVM classifiers is measured in terms of training effectiveness and classification accuracy. With 95% accuracy, it manages the process of brain tumor categorization in MRI scans. The efficacy of training and classification accuracy of the CNN classifier is compared (96.33%). Both methods get high accuracy but as compared to SVM, CNN provides more accuracy and consumes less time for execution.

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[1]
Dinesh M. Barode, Rupali S. Awhad, Vijay D. Dhangar, and Dr. Seema S. Kawathekar , Trans., “Comparative Analysis of SVM and CNN Techniques for Brain Tumor Detection”, IJITEE, vol. 13, no. 7, pp. 27–33, Jun. 2024, doi: 10.35940/ijitee.G9908.13070624.
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How to Cite

[1]
Dinesh M. Barode, Rupali S. Awhad, Vijay D. Dhangar, and Dr. Seema S. Kawathekar , Trans., “Comparative Analysis of SVM and CNN Techniques for Brain Tumor Detection”, IJITEE, vol. 13, no. 7, pp. 27–33, Jun. 2024, doi: 10.35940/ijitee.G9908.13070624.
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References

Imayanmosha Wahlang, Pallabi Sharma, Syeda Musfia Nasreen, Arnab Kumar Maji and Goutam Saha, “A Comparative Study on Segmentation Techniques for Brain Tumor MRI”, Springer Nature Singapore Pte Ltd. 2019.

B.Srikanth1 , Dr. E. Sreenivasa Reddy, “Advance Detection and Extraction of Brain Multi Tumor from MRI and its Morphological Operation Symmetric Analysis(MOSA)”,International Journal of Computer Science and Information Security (IJCSIS), Vol. 14, No. 12, December 2016.

M. Usman Akram', Anam Usma, “Computer Aided System for Brain Tumor Detection and Segmentation”, 978-1-61284-941-6/11/$26.00 ©2011 IEEE

T. Kalaiselvi, “Brain Tumor Boundary Detection by Edge Indication Map Using Bi-Modal Fuzzy Histogram Thresholding Technique from MRI T2-Weighted”, I.J. Image, Graphics and Signal Processing, 2016, 9, 51-59 Published Online September 2016 in MECS (http://www.mecs-press.org/) DOI: 10.5815/ijigsp.2016.09.07. https://doi.org/10.5815/ijigsp.2016.09.07

Narkhede Sachin, Dr. Deven Shah, Prof. Vaishali khairnar, Prof. Sujata Kadu, “Brain Tumor Detection Based on Bilateral Symmetry Information”, Int Journal of Engineering Research and Applications www.ijera.com ISSN : 2248-9622, Vol. 4, Issue 6( Version 3), June 2014, pp.98-103.

Moitra D, Mandal R, “Review of Brain Tumor Detection using Pattern Recognition Techniques”, International Journal of Computer Sciences and Engineering Review Paper Volume-5, Issue-2 E-ISSN: 2347-2693 28/Feb/2017.

Borole VY, Kawathekar S. S., “Study of various DIP Techniques used for Brain Tumor detection and tumor area calculation using MRI images”, International Journal of Computer Sciences and Engineering. 2016 Jul;4(7):39-43.F

Nithyasree C, Stanley D, Subalakshmi K, “Brain Tumor Detection Using Image Processing”, International Journal on Cybernetics & Informatics (IJCI) Vol. 10, No.1/2, May 2021.

Telrandhe SR, Pimpalkar A, Kendhe A,“Brain tumor detection using object labeling algorithm & SVM”, International Engineering Journal For Research & Development. 2015 Nov;2:2-8.

Kumar TS, Rashmi K, Ramadoss S, Sandhya LK, Sangeetha TJ, “Brain tumor detection using SVM classifier”, In 2017 Third International Conference on Sensing, Signal Processing and Security (ICSSS) 2017 May 4 (pp. 318-323). IEEE.

Rajesh C. patil, A.S. Bhalchandra, “Brain tumor extraction from MRI images Using MATLAB”, IJECSCSE, Volume 2, issue1.

Amer Al-Badarneh, Hassan Najadat, Ali M. Alraziqi, “A Classifier to Detect Tumor Disease in MRI Brain Images”, IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, (ASONAM-2012).142, 2012.

Mohammed Y. Kamil, “Brain Tumor Area Calculation in CT scan image using Morphological Operations”, IOSR Journal of Computer Engineering,Volume 17, Issue 2(V), Page No. 125-128, Mar – Apr. 2015.

Imayanmosha Wahlang, Pallabi Sharma, Syeda Musfia Nasreen, Arnab Kumar Maji, and Goutam Saha, “A Comparative Study on Segmentation Techniques for Brain Tumor MRI” Springer Nature Singapore, 31 August 2018, 978-981-13-0585

Nagalkar V. J, Sarate G.G., “Brain Tumor Detection and Identification using Support Vector Machine”, International Research Journal of Engineering and Technology (IRJET). 2019 Dec;4(07).

Vishal S. Shirsat, Seema S. Kawathekar, 2014, “Classification of Brain Cancer Detection by using Magnetic Resonance Imaging”, INTERNATIONAL JOURNAL OF ENGINEERING RESEARCH & TECHNOLOGY (IJERT) Volume 03, Issue 02 (February 2014).

Ruria, S., Gautam, P., Raj, A., & Pandey, G. (2024). Brain Tumor Detection System using Deep Learning. In International Journal of Innovative Technology and Exploring Engineering (Vol. 13, Issue 3, pp. 23–27). https://doi.org/10.35940/ijitee.h9678.13030224

Shetty, S., & Shetty, J. (2020). Classification of Brain Tumor using Convolutional Neural Networks. In International Journal of Engineering and Advanced Technology (Vol. 9, Issue 3, pp. 2841–2845). https://doi.org/10.35940/ijeat.c5995.029320

Kumar K., S., Kumar R., A., S., S., R., D., & V., D. (2020). Brain Tumor Classification using Convolution Neural Network and Size Estimation by Marker Based Watershed Segmentation. In International Journal of Recent Technology and Engineering (IJRTE) (Vol. 8, Issue 6, pp. 3633–3637). https://doi.org/10.35940/ijrte.f8967.038620

Nagar, K., & Chawla, M. P. S. (2023). A Survey on Various Approaches for Support Vector Machine Based Engineering Applications. In International Journal of Emerging Science and Engineering (Vol. 11, Issue 11, pp. 6–11). https://doi.org/10.35940/ijese.k2555.10111123

Sistla, S. (2022). Predicting Diabetes u sing SVM Implemented by Machine Learning. In International Journal of Soft Computing and Engineering (Vol. 12, Issue 2, pp. 16–18). https://doi.org/10.35940/ijsce.b3557.0512222