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Recent Advances in Inflammation & Allergy Drug Discovery

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ISSN (Print): 2772-2708
ISSN (Online): 2772-2716

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Research Article

A Novel Detection of Cerebrovascular Disease using Multimodal Medical Image Fusion

Author(s): Sudip Paul and Shruti Jain*

Volume 18, Issue 2, 2024

Published on: 19 April, 2024

Page: [140 - 155] Pages: 16

DOI: 10.2174/0127722708288426240408042054

Price: $65

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Abstract

Background: Diseases are medical situations that are allied with specific signs and symptoms. A disease may be instigated by internal dysfunction or external factors like pathogens. Cerebrovascular disease can progress from diverse causes, comprising thrombosis, atherosclerosis, cerebral venous thrombosis, or embolic arterial blood clot.

Objective: In this paper, authors have proposed a robust framework for the detection of cerebrovascular diseases employing two different proposals which were validated by use of other datasets.

Methods: In proposed model 1, the Discrete Fourier transform is used for the fusion of CT and MR images which was classified them using machine learning techniques and pre-trained models while in proposed model 2, the cascaded model was proposed. The performance evaluation parameters like accuracy and losses were evaluated.

Results: 92% accuracy was obtained using Support Vector Machine using Gray Level Difference Statistics and Shape features with Principal Component Analysis as a feature selection technique while Inception V3 resulted in 95.6% accuracy while the cascaded model resulted in 96.21% accuracy.

Conclusion: The cascaded model is later validated on other datasets which results in 0.11% and 0.14% accuracy improvement over TCIA and BRaTS datasets respectively.

Keywords: Cerebrovascular diseases, medical imaging modalities, medical image fusion, pre-trained models, feature extraction, SVM.

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[1]
Clinical Methods: The History, Physical, and Laboratory Examinations 3rd. 1990. Available from: https://www.ncbi.nlm.nih.gov/books/NBK378/
[2]
Cerebrovascular Disease. Available from: https://www.aans.org/en/Patients/Neurosurgical-Conditions-and-Treatments/Cerebrovascular-Disease
[3]
Mohan G, Subashini MM. Medical Imaging with Intelligent Systems: A Review In: Deep Learning and Parallel Computing Environment for Bioengineering Systems. Academic Press 2019; 22: pp. (10)53-73.
[4]
Reblin M, Sahebjam S, Peeri NC, Martinez YC, Thompson Z, Egan KM. Medical cannabis use in glioma patients treated at a comprehensive cancer center in florida. J Palliat Med 2019; 22(10): 1202-7.
[http://dx.doi.org/10.1089/jpm.2018.0528] [PMID: 31081711]
[5]
Kohler BA, Ward E, McCarthy BJ, et al. Annual report to the nation on the status of cancer, 1975-2007, featuring tumors of the brain and other nervous system. J Natl Cancer Inst 2011; 103(9): 714-36.
[http://dx.doi.org/10.1093/jnci/djr077] [PMID: 21454908]
[6]
Herrera SL. Inferring axon diameters using magnetic resonance imaging oscillating gradient spin echo sequences. Magn ResonImag 2018; 64-70.
[7]
Jin J. Electromagnetic analysis and design in magnetic resonance imaging. In: Engineering & Technology. New York: Routledge 2018; pp. 1-282.
[http://dx.doi.org/10.1201/9780203758731]
[8]
Despotović I, Goossens B, Philips W. MRI segmentation of the human brain: challenges, methods, and applications. Comput Math Methods Med 2015; 2015: 1-23.
[http://dx.doi.org/10.1155/2015/450341] [PMID: 25945121]
[9]
Skogen K, Schulz A, Dormagen JB, Ganeshan B, Helseth E, Server A. Diagnostic performance of texture analysis on MRI in grading cerebral gliomas. Eur J Radiol 2016; 85(4): 824-9.
[http://dx.doi.org/10.1016/j.ejrad.2016.01.013] [PMID: 26971430]
[10]
Wang X, Wang D, Yao Z, et al. Machine learning models for multi parametric glioma grading with quantitative result interpretations. Front Neurosci 2018; 12: 1046.
[11]
Vijan A, Dubey P, Jain S. Comparative Analysis of Various Image Fusion Techniques for Brain Magnetic Resonance Images. Procedia Comput Sci 2020; 167: 413-22.
[http://dx.doi.org/10.1016/j.procs.2020.03.250]
[12]
Pal B, Mahajan S, Jain S. A comparative study of traditional image fusion techniques with a novel hybrid method. International Conference on Computational Performance Evaluation (ComPE). North Eastern Hill University. Shillong, Meghalaya, India. 2020; pp. 820-5.
[http://dx.doi.org/10.1109/ComPE49325.2020.9200017]
[13]
Pal B, Mahajan S, Jain S. Medical image fusion employing enhancement techniques Conference on Electrical and Computer Engineering (WIECON-ECE). Bangladesh Dec 26-27, 2020;.
[14]
Li S, Kang X, Fang L, Hu J, Yin H. Pixel-level image fusion: A survey of the state of the art. Inf Fusion 2017; 33: 100-12.
[http://dx.doi.org/10.1016/j.inffus.2016.05.004]
[15]
Chen G, Ru J, Zhou Y, et al. MTANS: multi-scale mean teacher combined adversarial network with shape-aware embedding for semi-supervised brain lesion segmentation. Neuroimage 2021; 244: 118568.
[http://dx.doi.org/10.1016/j.neuroimage.2021.118568] [PMID: 34508895]
[16]
Tang Y, Wang S, Qu Y, Cui Z, Zhang W. Consistency and adversarial semi-supervised learning for medical image segmentation. Comput Biol Med 2023; 161: 107018.
[http://dx.doi.org/10.1016/j.compbiomed.2023.107018] [PMID: 37216776]
[17]
Xiao Z, Su Y, Deng Z, Zhang W. Efficient combination of cnn and transformer for dual-teacher uncertainty-guided semi-supervised medical image segmentation. Comput Methods Programs Biomed 2022; 226: 107099.
[http://dx.doi.org/10.1016/j.cmpb.2022.107099] [PMID: 36116398]
[18]
Jiao R, Zhang Y, Ding L, et al. Learning with limited annotations: A survey on deep semi-supervised learning for medical image segmentation. Comput Biol Med 2024; 169: 107840.
[http://dx.doi.org/10.1016/j.compbiomed.2023.107840] [PMID: 38157773]
[19]
Ren Z, Li Q, Cao K, Li MM, Zhou Y, Wang K. Model performance and interpretability of semi-supervised generative adversarial networks to predict oncogenic variants with unlabeled data. BMC Bioinformatics 2023; 24(1): 43.
[http://dx.doi.org/10.1186/s12859-023-05141-2] [PMID: 36759776]
[20]
Xu Y, Huang H, Heidari AA, et al. MFeature: Towards high performance evolutionary tools for feature selection. Expert Syst Appl 2021; 186: 115655.
[http://dx.doi.org/10.1016/j.eswa.2021.115655]
[21]
Salau AO, Jain S, Eneh JN. A review of various image fusion types and transforms. Indonesian Journal of Electrical Engineering and Computer Science 2021; 24(3): 1515-22.
[http://dx.doi.org/10.11591/ijeecs.v24.i3.pp1515-1522]
[22]
Bhardwaj C, Jain S, Sood M. Transfer learning based robust automatic detection system for diabetic retinopathy grading. Neural Comput Appl 2021; 33(20): 13999-4019.
[http://dx.doi.org/10.1007/s00521-021-06042-2]
[23]
Cao C, Liu F, Tan H, et al. Deep learning and its applications in biomedicine. Genomics Proteomics Bioinformatics 2018; 16(1): 17-32.
[http://dx.doi.org/10.1016/j.gpb.2017.07.003] [PMID: 29522900]
[24]
Henaff M, Bruna J, LeCun Y. Deep convolutional networks on graph-structured data. arXiv:150605163 2015.
[25]
Krizhevsky A, Sutskever I, Hinton GE. Imagenet classification with deep convolutional neural networks. In: NIPS’12: Proceedings of the 25th International Conference on Neural Information Processing Systems. ACM Digital Library 2012; 1: pp. 1097-105.
[26]
Lai S, Xu L, Liu K, Zhao J. Recurrent convolutional neural networks for text classification. Twenty-Ninth AAAI Conference on Artificial Intelligence 2025; 29(1)
[http://dx.doi.org/10.1609/aaai.v29i1.9513]
[27]
Harvard Medical School lecture notes: Introduction to Neuroimaging. Available from: http://www.med.harvard.edu/aanlib/home.html
[28]
Jain S. Computer-aided detection system for the classification of non-small cell lung lesions using SVM. Curr Computeraided Drug Des 2021; 16(6): 833-40.
[http://dx.doi.org/10.2174/1573409916666200102122021] [PMID: 31899680]
[29]
Li R, Zhang W, Suk HI, et al. Deep learning based imaging data completion for improved brain disease diagnosis. In: Proceedings of the Medical Image Computing and Computer Assisted Intervention MICCAI-BRATS. SpringerLink. 2014; pp. 305-12.
[http://dx.doi.org/10.1007/978-3-319-10443-0_39]
[30]
He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. Las Vegas, NV, USA. 2016; pp. 770-8.
[31]
Szegedy C, Liu W, Jia Y, et al. Going deeper with convolutions Proceedings of the IEEE conference on computer vision and pattern recognition. Boston, MA, USA. 2015; pp. 1-9.
[32]
Jin Z, Min L, Ng MK, Zheng M. Image colorization by fusion of color transfers based on DFT and variance features. Comput Math Appl 2019; 77(9): 2553-67.
[http://dx.doi.org/10.1016/j.camwa.2018.12.033]
[33]
Pal B, Jain S. Novel discrete component wavelet transform for detection of cerebrovascular diseases. Sadhana 2022; 47(4): 237.
[http://dx.doi.org/10.1007/s12046-022-02016-9]
[34]
Bajwa MN, Malik MI, Siddiqui SA, et al. Two-stage framework for optic disc localization and glaucoma classification in retinal fundus images using deep learning. BMC Med Inform Decis Mak 2019; 19(1)
[35]
Simonyan K, Zisserman A. Very deep convolutional networks for large-scale image recognition. ArXiv14091556Cs 2015.
[36]
Jain S, Saxena S, Sinha S. Ensemble architecture for prediction of grading of diabetic retinopathy. Cybern Syst 2022; 1-19.
[http://dx.doi.org/10.1080/01969722.2022.2151176]
[37]
Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z. Rethinking the inception architecture for computer vision. Proceedings of the IEEE conference on computer vision and pattern recognition. Las Vegas, NV, USA. 2016; pp. 2818-26.
[http://dx.doi.org/10.1109/CVPR.2016.308]

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