MRI Based Detection of Brain Tumor using Advanced Image Processing Techniques
Keywords:
Brain tumor, tumor detection, image processings, canny edge detectionAbstract
Brain tumor detection is essential for early diagnosis and treatment planning for patients. In recent years, image processing algorithms have evolved into practical instruments for automatic and trustworthy tumor diagnosis from medical imaging data. This study presents a novel method for image processing-based brain tumor detection. The suggested method employs a number of preprocessing steps to lower noise and enhance the clarity of brain images. Included are contrast enhancement, noise reduction, and image scaling. After preprocessing, the brain region is separated from the background using image segmentation methods, which also help to isolate potential tumor spots. To extract important properties from the segmented brain areas for tumor identification, several feature extraction techniques are applied. These criteria capture important characteristics of the tumor, like form, texture, and intensity variations. Following feature retrieval, a classifier is trained to differentiate between tumor and non-tumor regions. To evaluate the effectiveness of the recommended method, experiments are conducted with a collection of brain images that includes both tumor and non-tumor cases. The results demonstrate the excellent accuracy and efficiency of the suggested approach for brain tumor detection. The benefits of the proposed strategy in terms of computing efficiency and detection accuracy are shown through comparisons with current methods. All things considered, the proposed image-based brain tumor detection system holds great potential to assist medical practitioners in the early detection of brain cancers. It can lead to better patient outcomes by enabling prompt intervention and customized care.
Downloads
References
Shubhangi Solanki, Uday Pratap Singh, Siddharth Singh Chouhan, and Sanjeev Jain, “Brain Cancer Detection and Classification Using Intelligence Techniques: An Overview”- 2023 IEEE
Md Ishtyaq Mahmud,Muntasir Mamun, and Ahmed Abdelgawad, “A Deep Analysis of Brain Cancer Detection from MR Images Using Deep Learning Networks” - 2023 MDPI
Aqsa Tahir, Muhammad Asif, Maaz Bin Ahmad, Toqeer Mahmood, Muhammad Adnan Khan, and Mushtaq Ali6, "Brain Cancer Detection using Decision-Based Fusion Empowered with Fuzzy Logic" – 2022 Hindawi
Anjanayya S, V. M. Gayathri, and R. Pitchai,"Brain Cancer Segmentation and Survival Prediction using Multimodal MRI Scans with Deep learning Algorithms" – 2022 IEEE
Yue Zhang, Benxiang Jiang, Jiong Wu, Dongcen Ji, Yilong Liu, Yifan Chen, Ed X. Wu, and Xiaoying Tang, “Deep Learning Initialized and Gradient Enhanced Level-Set Based Segmentation for Liver Tumor From CT Images” – 2020 IEEE
Tao Lei, Risheng Wang, Yuxiao Zhang, Yong Wan, Chang Liu, and Asoke K. Nandi, “DefED-Net: Deformable Encoder-Decoder Network for Liver and Liver Tumor Segmentation”. – 2022 IEEE
Xin Dong, Yizhao Zhou, Lantian Wang, Jingfeng Peng, Yanbo Lou, and Yiqun Fan, “Liver Cancer Detection Using Hybridized Fully Convolutional Neural Network Based on Deep Learning Framework”. – 2020 IEEE
Simona Turco, Thodsawit Tiyarattanachai, Kambez Ebrahimkheil, John Eisenbrey, Aya Kamaya, Massimo Mischi, Senior Member, Andrej Lyshchik, and Ahmed El Kaffas, “Interpretable Machine Learning for Characterization of Focal Liver Lesions by Contrast- Enhanced Ultrasound”. – 2022 IEEE
Sowjanya M. N, Stafford Michahia, Mahanthesha U, and Nagalakshmi T. S,"Lung Cancer Detection in Chest X - Ray Image". – 20 IEEE
Dr. H S Mohan and Mahanthesha U, “Human action Recognition using STIP Techniques”, International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN: 2278-3075, Volume-9 Issue-7, May 2020
J. F. Allen, “Maintaining knowledge about temporal intervals,” Commun. ACM, vol. 26, no. 11, pp. 832–843, Nov. 1983.
C. Fernandez, P. Baiget, X. Roca, and J. Gonzalez, “Interpretation of complex situations in a semantic-based surveillance framework,” Image Commun., vol. 23, no. 7, pp. 554–569, Aug. 2008.
J. Candamo, M. Shreve, D. B. Goldgof, D. B. Sapper, and R. Kasturi, “Understanding transit scenes: A survey on human behavior-recognition algorithms,” IEEE Trans. Intell. Transp. Syst., vol. 11, no. 1, pp. 206–224, Mar. 2010.
Y. Changjiang, R. Duraiswami, and L. Davis, “Fast multiple object tracking via a hierarchical particle filter,” in Proc. 10th IEEE ICCV, 2005, vol. 1, pp. 212–219.
A. Loza, W. Fanglin, Y. Jie, and L. Mihaylova, “Video object tracking with differential Structural SIMilarity index,” in Proc. IEEE ICASSP, 2011, pp. 1405–1408.
D. Comaniciu, V. Ramesh, and P. Meer, “Kernel-based object tracking,” IEEE Trans. Pattern Anal. Mach. Intell., vol. 25, no. 5, pp. 564–577, May 2003.
V. Papadourakis and A. Argyros, “Multiple objects tracking in the presence of long-term occlusions,” Comput. Vis. Image Underst., vol. 114, no. 7, pp. 835–846, Jul. 2010.
Mahanthesh U, Dr. H S Mohana “Identification of Human Facial Expression Signal Classification Using Spatial Temporal Algorithm” International Journal of Engineering Research in Electrical and Electronic Engineering (IJEREEE) Vol 2, Issue 5, May 2016
NikiEfthymiou, Petros Koutras, Panagiotis, Paraskevas, Filntisis, Gerasimos Potamianos, Petros Maragos “Multi-View Fusion for Action Recognition in Child-Robot Interaction”: 978-1-4799-7061-2/18/$31.00 ©2018 IEEE.
Nweke Henry Friday, Ghulam Mujtaba, Mohammed Ali Al-garadi, Uzoma Rita Alo, analysed “Deep Learning Fusion Conceptual Frameworks for Complex Human Activity Recognition Using Mobile and Wearable Sensors”: 978-1-5386-1370-2/18/$31.00 ©2018 IEEE.
.Van-Minh Khong, Thanh-Hai Tran, ”Improving human action recognition with two-stream 3D convolutional neural network”, 978-1-5386-4180-4/18/$31.00 ©2018 IEEE.
Pavithra S, Mahanthesh U, Stafford Michahial, Dr. M Shivakumar, “Human Motion Detection and Tracking for Real-Time Security System”, International Journal of Advanced Research in Computer and Communication Engineering ISO 3297:2007 Certified Vol. 5, Issue 12, December 2016.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.
