Improved Brain Tumor Diagnosis and Classification Using VMS
Keywords:
brain tumors, Glioma, Meningioma, Metastasis, Astrocytoma, early detection, accurate classification, VMS Integ-Net model, deep learning, machine learning, accuracy, specificity, sensitivity, diagnosis, healthcareAbstract
Brain tumors pose a significant challenge in medical imaging due to their variability and the need for precise classification. Our study aims to address this problem by developing a new method for detecting and classifying brain tumors. The key goal of this research is to improve brain tumor classification accuracy in medical imaging. This research is motivated by the need for accurate brain tumor diagnosis and treatment planning. The BRATS dataset, which contains 1000 images of four different types of brain tumors: glioma, meningioma, metastasis, and astrocytoma, was used in our study. In this research, we use the advanced VMS Integ-Net model to address the critical challenge of brain tumor detection and classification. The VMS Integ-Net model is unique in that it combines the HoG-based Feature extraction stage with the VGG-19's feature extraction, as well as its capability with Multi-Class Support Vector Machine (SVM) for robust and efficient classification. With an astounding accuracy rate of 99.57%, our method was an enormous accomplishment. It had a high sensitivity of 99.3405% and a high specificity of 99.3523%. These findings outperform previously published methods, emphasizing the significance of our work in advancing brain tumor diagnosis and classification. This major study advances medical decision-making and opens the door to automated diagnostic solutions in neurology and oncology.
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References
Medical Imaging in Cancer Care: Charting the Progress, US Oncology and National Electrical Manufacturers Association (2012)
Mishra PK, Satapathy SC, Rout M (2021) Segmentation of MRI Brain Tumor Image using Optimization based Deep Convolutional Neural networks (DCNN). Open Comput Sci 11:380–390
Parihar AS (2017) A Study on Brain Tumor Segmentation Using Convolution Neural Network. 2017 International Conference on Inventive Computing and Informatics (ICICI)
Rammurthy D, Mahesh PK (2020) Whale Harris Hawks optimization based deep learning classifer for brain tumor detection using MRI images. J King Saud Univ Comput Inf Sci 1–14.
Reza, S.; Iftekharuddin, K.M. Improved brain tumor tissue segmentation using texture features. In Proceedings of the MICCAI BraTS (Brain Tumor Segmentation Challenge), Boston, MA, USA, 14 September 2014; pp. 27–30
Ramtekkar PK, Pandey A, Pawar MK (2020) A proposed model for automation of detection and classifcation of brain tumor by deep learning. 2020 2nd International Conference on Data, Engineering and Applications (IDEA).
Ramtekkar PK, Pandey A, Pawar MK (2023) Innovative brain tumor detection using optimized deep learning techniques. Int J Syst Assur Eng Manag 14:459–473
Ruan, S.; Lebonvallet, S.; Merabet, A.; Constans, J.-M. Tumor segmentation from a multispectral MRI images by using support vector machine classification. In Proceedings of the 2007 4th IEEE International Symposium on Biomedical Imaging: From Nano to Macro, Arlington, VA, USA, 12–15 April 2007; pp. 1236–1239.
A Tiwari A, Srivastava S, Pant M. Brain Tumor segmentation and classifcation from magnetic resonance images: Review of selected methods from 2014 to 2019. Pattern Recognition Letters. 2020;131:244–60. https://doi. org/10.1016/j.patrec.2019.11.020
Munir K, Frezza F, Rizzi A. Brain Tumor segmentation using 2D-UNET convolutional neural network. Deep Learning for Cancer Diagnosis. 2021:239–48. https://doi.org/10.1007/978-981-15-6321-8_14
Aher P, Lilhore U. Survey of brain Tumor image quarrying techniques. Int J Sci Eng Dev Res, ISSN. 2020:2455–631. 4. Zhang D, Huang G, Zhang Q, Han J, Han J, Yu Y. Cross-modality deep feature learning for brain Tumor segmentation. Pattern Recogn. 2021;1(110). https://doi.org/10.1016/j.patcog.2020.107562
S. Mohsin, S. Sajjad, Z. Malik, and A. H. Abdullah, “Efficient way of skull stripping in MRI to detect brain tumor by applying morphological operations, after detection of false background,” International Journal of Information and Education Technology, vol. 2, no. 4, pp. 335–337, 2012.
B. Willmore, R. J. Prenger, M. C. Wu, and J. L. Gallant, “The Berkeley wavelet transform: a biologically inspired orthogonal wavelet transform,” Neural Computation, vol. 20, no. 6, pp. 1537– 1564, 2008.
P. Remya Ravindran and K. P. Soman, “Berkeley wavelet transform based image watermarking,” in Proceedings of the International Conference on Advances in Recent Technologies in Communication and Computing (ARTCom ’09), pp. 357–359, IEEE, Kerala, India, October 2009.
M. Alwan and E. M. Jamel, “Digital image watermarking using Arnold scrambling and Berkeley wavelet transform,” AlKhwarizmi Engineering Journal, vol. 12, pp. 124–133, 2015.
R. M. Haralick, K. Shanmugam, and I. Dinstein, “Textural features for image classification,” IEEE Transactions on Systems, Man and Cybernetics, vol. 3, no. 6, pp. 610–621, 1973.
J. Liu, M. Li, J.Wang, F.Wu, T. Liu, and Y. Pan, “A survey of MRIbased brain tumor segmentation methods,” Tsinghua Science and Technology, vol. 19, no. 6, pp. 578–595, 2014.
B.H. Menze, A. Jakab, S. Bauer, J. Kalpathy-Cramer, K. Farahani, J. Kirby, Y. Burren, N. Porz, J. Slotboom, R. Wiest, L. Lanczi, E. Gerstner, M.A. Weber, T. Arbel, B.B. Avants, N. Ayache, P. Buendia, D.L. Collins, N. Cordier, J.J. Corso, A. Criminisi, T. Das, H. Delingette, Ç. Demiralp, C.R. Durst, M. Dojat, S. Doyle, J. Festa, F. Forbes, E. Geremia, B. Glocker, P. Golland, X. Guo, A. Hamamci, K.M. Iftekharuddin, R. Jena, N.M. John, E. Konukoglu, D. Lashkari, J.A. Mariz, R. Meier, S. Pereira, D. Precup, S.J. Price, T.R. Raviv, S.M. Reza, M. Ryan, D. Sarikaya, L. Schwartz, H.C. Shin, J. Shotton, C.A. Silva, N. Sousa, N.K. Subbanna, G. Szekely, T.J. Taylor, O.M. Thomas, N.J. Tustison, G. Unal, F. Vasseur, M. Wintermark, D.H. Ye, L. Zhao, B. Zhao, D. Zikic, M. Prastawa, M. Reyes, K. Van Leemput. "The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS)." IEEE Transactions on Medical Imaging, Vol. 34, No. 10, pp. 1993-2024, October 2015. doi: 10.1109/TMI.2014.2377694. Epub 2014 Dec 4. PMID: 25494501; PMCID: PMC4833122.
Ahmed, B.T. Lung Cancer Prediction and Detection Using Image Processing Mechanisms: An Overview. Signal Image Process. Lett. (SIMPLE) 2019, 1, 20–31
S. Roy and S.K. Bandyopadhyay, "Detection and Quantification of Brain Tumor from MRI of Brain and its Symmetric Analysis", International Journal of Information and Communication Technology Research, vol. 2, 2012.
Sharma AK, Tiwari S, Aggarwal G,Goenka N, Kumar A, Chakrabarti P, Chakrabarti T, Gono R, Leonowicz Z, Jasiński M (2022) Dermatologist-Level Classification of Skin Cancer Using Cascaded Ensembling of Convolutional Neural Network and Handcrafted Features Based Deep Neural Network. IEEE Access. https://doi.org/10.1109/ACCESS.2022.3149824
Zhang, J., Zhou, H., Niu, Y., Lv, J., Chen, J., & Cheng, Y. (2021). CNN and multi-feature extraction based denoising of CT images. Biomed. Signal Process. Control., 67, 102545.
Hossain T, Shadmani Shishir F, Ashraf M, Abdullah Al Nasim MD, Muhammad Shah F (2019) Brain Tumor Detection Using Convolutional Neural Network, 1st International Conference on Advances in Science, Engineering and Robotics Technology (ICASERT-2019)
Khalil, H.A.; Darwish, S.; Ibrahim, Y.M.; Hassan, O.F. 3D-MRI brain tumor detection model using modified version of level set segmentation based on dragonfly algorithm. Symmetry 2020, 12, 1256.
Avsar E, Salcin K (2019) Detection and classification of brain tumors from MRI images using faster R-CNN. Tehnički Glasnik 13(4):337–342
Naseer I, Akram S, Masood T, Jaffar A, Khan MA, Mosavi A. Performance analysis of state-of-the-art cnn architectures for luna16. Sensors. 2022;22(12):4426. doi: 10.3390/s22124426.
J. Ramasamy, R. Doshi and K. K. Hiran, "Detection of Brain Tumor in Medical Images Based on Feature Extraction by HOG and Machine Learning Algorithms," 2022 International Conference on Trends in Quantum Computing and Emerging Business Technologies (TQCEBT), Pune, India, 2022, pp. 1-5, doi: 10.1109/TQCEBT54229.2022.10041564.
B. Sathees Kumar and R. Anbu Selvi, "Feature extraction using image mining techniques to identify brain tumors", ICIIECS 2015 2015 IEEE Int. Conf. Innov. Information Embed. Commun. Syst, 2015.
H. Habib, R. Amin, B. Ahmed and A. Hannan, "Hybrid algorithms for brain tumor segmentation classification and feature extraction", Journal of Ambient Intelligence and Humanized Computing, vol. 13, no. 5, pp. 2763-2784, 2022.
S. E. Alavi and M. javad Rashti, "Brain tumors detection on mri images through extracting hog features", Journal Of Advanced Applied Scientific Research, vol. 2, no. 1, pp. 9-25, 2018.
Unal, M.O., Ertas, M., & Yildirim, I. (2020). An unsupervised reconstruction method for low-dose CT using deep generative regularization prior. Biomed. Signal Process. Control., 75, 103598.
Y. Ma, B. Wei, P. Feng, P. He, X. Guo and G. Wang, "Low-Dose CT Image Denoising Using a Generative Adversarial Network With a Hybrid Loss Function for Noise Learning," in IEEE Access, vol. 8, pp. 67519-67529, 2020, doi: 10.1109/ACCESS.2020.2986388.
Rammurthy D, Mahesh PK (2020) Whale Harris Hawks optimization based deep learning classifier for brain tumor detection using MRI images. J King Saud Univ Comput Inf Sci 1–14
Aggarwal, M., Tiwari, A.K., Sarathi, M. et al. An early detection and segmentation of Brain Tumor using Deep Neural Network. BMC Med Inform Decis Mak 23, 78 (2023). https://doi.org/10.1186/s12911-023-02174-8
Geetha M, Prasanna Lakshmi K, Sajeev Ram Arumugam & Sandhya N (2023) Conditional random field-recurrent neural network segmentation with optimized deep learning for brain tumour classification using magnetic resonance imaging, The Imaging Science Journal, DOI: 10.1080/13682199.2023.2178611
Sivapathi Arunachalam & Gopalakrishnan Sethumathavan (2022) An effective tumor detection in MR brain images based on deep CNN approach: i-YOLOV5, Applied Artificial Intelligence, 36:1, DOI: 10.1080/08839514.2022.2151180
Salabha Varghese, “Brain Tumor Segmentation by FCM and Enhancement by ANN, using GLCM Based Feature Extraction”, International Journal of Science and Research (IJSR),vol 3,Issue 11, November 2014.
P. Vasuda and S. Satheesh, "Improved Fuzzy C-Means Algorithm for MR Brain Image Segmentation", International Journal on Computer Science and Engineering(IJCSE), vol. 02, no. 05, pp. 1713-1715, 2010.
K. Honda, H. Ichihashi and S. Miyamoto, Algorithms for Fuzzy Clustering Methods in c-Means Clustering with Applications, Berlin Heidelberg:Springer-Verlag, 2008.
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