Research Article |
Brain Tumor Detection using Improved Binomial Thresholding Segmentation and Sparse Bayesian Extreme Learning Machine Classification
Author(s): Prasadu Reddi*, Gorla Srinivas, P.V.G.D. Prasad Reddy and Harshitha Sai Nallagonda
Published In : International Journal of Electrical and Electronics Research (IJEER) Volume 12, Issue 3
Publisher : FOREX Publication
Published : 30 September 2024
e-ISSN : 2347-470X
Page(s) : 1094-1100
Abstract
People are dying these days from numerous deadliest diseases. One such illness is brain tumour, in which the unusual cells within the tumour quickly begin to damage the brain's healthy cells. Owing to this rapid growth, a person may pass away before the disease receives a correct diagnosis. Early disease detection is essential for any disease to help save the patient by providing them with better care. In a similar vein, a patient's life depends on early brain tumour detection. Brain tumour detection is an extremely challenging procedure that we would like to simplify in order to save time. The proposed model facilitates the quicker and more accurate identification of abnormal brain cells, leading to the early detection of brain tumours. In this work, an improved binomial thresholding-based segmentation (IBTBS) is introduced for segmentation purpose. From this segmented image, information theoretic based, wavelet transform (WT) based, and wavelet scattering transform (WST) based features are extracted. An optimization-based feature selection approach (OBFSA) is incorporated between feature selection and tumour classification in order to reduce the dimension of this retrieved feature. Finally, classification is performed using the Sparse Bayesian extreme learning machine (SBELM) classifier. The execution process of this proposed methodology takes an MRI image from the free accessible source. By computing and detecting four different parameters, the experimental analysis of the proposed approach displays the accuracy, specificity, and sensitivity values. This model can assist us in quickly diagnosing brain tumours, potentially saving the lives of patients.
Keywords: Segmentation
, Feature Extraction
, Feature Selection
, Classifier
.
Prasadu Reddi*, Research Scholar, AU-TDR-HUB, Computer Science & Systems Engineering, Andhra University, Visakhapatnam; Email: reddiprasad0112@gmail.com
Gorla Srinivas, Computer Science & Engineering, GITAM Deemed to be University, Visakhapatnam, AP; Email: srinivas.gitam@gmail.com
P.V.G.D. Prasad Reddy, Computer Science & Systems Engineering, Andhra University, Visakhapatnam, AP; Email: prasadreddy.vizag@gmail.com
Harshitha Sai Nallagonda, Computer Science & Engineering, GITAM Deemed to be University, Visakhapatnam, AP; Email: hnallago@gitam.in
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[1] Usman, K., & Rajpoot, K. (2017). Utilizing Wavelet Transform and Machine Learning for Brain Tumor Classification from Multi-Modality MRI. Pattern Analysis and Applications, 20, 871-881.
-
[2] Amin, J., Sharif, M., Gul, N., Yasmin, M., & Shad, S. A. (2020). Convolutional Neural Network-Based Brain Tumor Classification Using Discrete Wavelet Transform Fusion of MRI Sequences. Pattern Recognition Letters, 129, 115-122.
-
[3] Raju, A. R., Suresh, P., & Rao, R. R. (2018). Bayesian Fuzzy Clustering Approach for Brain Tumor Segmentation and Classification using Multi-SVNN. Biocybernetics and Biomedical Engineering, 38(3), 646-660.
-
[4] Banerjee, S., & Singh, G. K. (2021). Quality-Aware Compression of Multilead Electrocardiogram Signal via 2-Mode Tucker Decomposition and Steganography. Biomedical Signal Processing and Control, 64, 102230.
-
[5] Dasari, S. K., Gorla, S., & PVGD, P. R. (2023). Stacking Ensemble Approach for Identifying Informative Tweets on Twitter Data. International Journal of Information Technology, pp.1-12.
-
[6] Krishna, D. S., Gorla, S., & PRPVG, D. (2023). Disaster Tweet Classification: Majority Voting Approach using Machine Learning Algorithms. Intelligent Decision Technologies, 17(2), pp. 343-355.
-
[7] Krishna, D. S., Srinivas, G., & Reddy, P. P. (2024). A Deep Parallel Hybrid Fusion Model for disaster tweet classification on Twitter data. Decision Analytics Journal, 11, 100453.
-
[8] Dasari, S. K., Srinivas, G., & Reddy, P. P. (2023). A Comprehensive Study on Disaster Tweet Classification on Social Media Information. Soft Computing and Signal Processing, 1, 507.
-
[9] Dasari, S. K., Sravani, L., Kumar, M. U., & Rama Venkata Sai, N. (2023, May). Image Enhancement of Underwater Images Using Deep Learning Techniques. In International Conference on Data Analytics and Insights (pp. 715-730). Singapore: Springer Nature Singapore.
-
[10] Dasari, S. K., Kella, S. K., & Manda, R. (2023, March). Enhancing Environmental Sounds Classification through Deep Learning Techniques. In 2023 International Conference on Sustainable Computing and Data Communication Systems (ICSCDS) (pp. 265-270). IEEE.
-
[11] Krishna, D. S., Srinivas, G., & Reddy, P. V. G. D. (2023). Novel private cloud architecture: A three-tier approach to deploy private cloud using virtual machine manager. Intelligent Decision Technologies, 17(2), 275-285.