Stroke Lesion Prediction by Bille-Viper-Segmentation with Tandem-MU-net Model

doi:10.37015/AUDT.2025.240011

Abstract

Abstract:

Stroke is a critical condition marked by the death of brain cells due to inadequate blood flow, necessitating improved predictive models for stroke lesions. The accuracy and flexibility required to forecast and classify stroke lesions is lacking in current approaches, which compromise patient outcomes. To solve these issues, Bille-Viper-Segmentation with the Tandem-MU-Net Model is suggested as a solution for tissue damage detection problems. This study improves blood flow detection in stroke images by introducing the Bille-Viper-Segmentation method to overcome difficulties in recognizing tissue injury. This novel method effectively samples pixel data and analyzes fogging phases related to stroke lesions by utilizing a Deep Luxe Gauging Tree. Existing methods struggle with flexibility in varying conditions; thus, the Trans-Lucent-Rich Reprise Pattern recognition algorithm for precise identification of infected areas is introduced. Furthermore, the Focus View Algorithm is suggested, which incorporates features from infarcted regions to improve early detection of emerging lesions. Furthermore, the Tandem-MU-Net model is used to extract essential morphological features and categorize stroke types, including Hemorrhagic and Acute strokes, through an investigation of their neutral and ionic forms. The results show that the suggested model performs substantially better than existing methods, achieving an amazing accuracy rate of 75%, recall rate of 83%, F1 score of 98%, Dice score of 98%, and precision of 73%, all while operating effectively in a time frame of 250 seconds.

Key words: Segmentation; Tissue damage; Fogging phase; Infarcted area; Stroke lesion; Feature extraction

Beevi Fathima, N Santhi Dr, N Ramasamy Dr. Stroke Lesion Prediction by Bille-Viper-Segmentation with Tandem-MU-net Model. Advanced Ultrasound in Diagnosis and Therapy, 2025, 9(1): 65-78.

Figures/Tables 9

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Figure 7

Figure 8

Figure 9

References 32

[1]	Park SJ, Hussain I, Hong S, Kim D, Park H, & Benjamin HCM. Real-time gait monitoring system for consumer stroke prediction service. In 2020 IEEE International conference on consumer electronics (ICCE) 2020;1-4.
[2]	Hui H, Zhang X, Li F, Mei X, Guo Y. Automated ischemic stroke subtyping based on machine learning approach. IEEE Access 2020;8:118426-118432.
[3]	Yuan S, Wang J, Jiang Q, He Z, Huang Y, Li Z, et al. Long-term exposure to PM2. 5 and stroke: a systematic review and meta-analysis of cohort studies. Environmental research 2019;177:108587.
[4]	Lu Q, Mårtensson J, Zhao Y, Johansson L. Living on the edge: Family caregivers' experiences of caring for post-stroke family members in China: A qualitative study. Int J Nurs Stud 2019;94:1-8.
[5]	Wang G, Song T, Dong Q, Cui M, Huang N, Zhang S. Automatic ischemic stroke lesion segmentation from computed tomography perfusion images by image synthesis and attention-based deep neural networks. Med Image Anal 2020;65:101787.
[6]	Zhao B, Liu Z, Liu G, Cao C, Jin S, Wu H, Ding S. Deep learning-based acute ischemic stroke lesion segmentation method on multimodal MR images using a few fully labeled subjects. Comput Math Methods Med 2021;2021:3628179.
[7]	Sirsat M.S., Fermé E., Camara J. Machine learning for brain stroke: a review. Journal of Stroke and Cerebrovascular Diseases 2020;29:105162.
[8]	Liu T, Fan W, Wu C. A hybrid machine learning approach to cerebral stroke prediction based on imbalanced medical dataset. Artif Intell Med 2019;101:101723.
[9]	Heo TS, Kim YS, Choi JM, Jeong YS, Seo SY, Lee JH, Jeon JP, Kim C. Prediction of stroke outcome using natural language processing-based machine learning of radiology report of brain MRI. J Pers Med 2020;10:286.
[10]	Pal P, Nandal M, Dikshit S, Thusu A, Singh HV. Harnessing the power of ensemble machine learning for the heart stroke classification. EAI Endorsed Transactions on Pervasive Health and Technology 2023;9.
[11]	Sailasya G. and Kumari G.L.A. Analyzing the performance of stroke prediction using ML classification algorithms. International Journal of Advanced Computer Science and Applications 2021:12.
[12]	Alotaibi N.S., Alotaibi A.S., Eliazer M., Srinivasulu A. Detection of ischemic stroke tissue fate from the MRI images using a deep learning approach. Mobile Information Systems 2022:9399876.
[13]	Badriyah T., Sakinah N., Syarif I. and Syarif D.R. Machine learning algorithm for stroke disease classification. In 2020 International Conference on Electrical, Communication, and Computer Engineering (ICECCE) (pp. 1-5).
[14]	Emon MU, Keya MS, Meghla TI, Rahman MM, Al Mamun MS, & Kaiser MS.Performance analysis of machine learning approaches in stroke prediction. In 2020 4th International Conference on Electronics, Communication and Aerospace Technology (ICECA) 2020;1464-1469.
[15]	Gillmann C, Peter L, Schmidt C, Saur D, Scheuermann G, Potel M. Visualizing multimodal deep learning for lesion prediction. IEEE Comput Graph Appl 2021;41:90-98.
[16]	Park S., Kim B.K., Han M.K., Hong J.H., Yum K.S., & Lee D.I. Deep learning for prediction of mechanism in acute ischemic stroke using brain MRI. Scientific Reports 2021.
[17]	Yu Y, Xie Y, Thamm T, Gong E, Ouyang J, Huang C, et al. Use of deep learning to predict final ischemic stroke lesions from initial magnetic resonance imaging. JAMA network open 2020;3:e200772-e200772.
[18]	Dritsas, & Trigka M. Stroke risk prediction with machine learning techniques. Sensors 2022;22:4670.
[19]	Govindarajan P, Soundarapandian RK, Gandomi AH, Patan R, Jayaraman P, & Manikandan R. Classification of stroke disease using machine learning algorithms. Neural Computing and Applications 2020;32:817-828.
[20]	Bandi V, Bhattacharyya D, and Midhunchakkravarthy D. Prediction of brain stroke severity using machine learning. Rev. d'Intelligence Artif 2020;34:753-761.
[21]	Krittanawong C, Virk HUH, Bangalore S, Wang Z, Johnson KW, Pinotti R, et al. Machine learning prediction in cardiovascular diseases: a meta-analysis. Scientific Reports 2020;10:1-11.
[22]	Wu Y, Fang Y. Stroke prediction with machine learning methods among older chinese. Int J Environ Res Public Health 2020;17:1828.
[23]	Tozlu C, Edwards D, Boes A, Labar D, Tsagaris KZ, Silverstein J, et al. Machine learning methods predict individual upper-limb motor impairment following therapy in chronic stroke. Neurorehabil Neural Repair 2020;34:428-439.
[24]	Nishi H, Oishi N, Ishii A, Ono I, Ogura T, Sunohara T, et al. Predicting clinical outcomes of large vessel occlusion before mechanical thrombectomy using machine learning. stroke. 2019;50:2379-2388.
[25]	Orfanoudaki A, Chesley E, Cadisch C, Stein B, Nouh A, Alberts MJ, Bertsimas D. Machine learning provides evidence that stroke risk is not linear: The non-linear Framingham stroke risk score. PLoS One 2020;15:e0232414.
[26]	Alagarsamy S, Reddy TTK, Kumar BP, Vineeth PS, & Kumar AS. A Novel Technique for Prophecy of Brain Strokes. In 2022 6th International Conference on Intelligent Computing and Control Systems (ICICCS) 2022;1203-1208.
[27]	Su SS, Li LY, Wang Y, Li YZ. Stroke risk prediction by color Doppler ultrasound of carotid artery-based deep learning using Inception V3 and VGG-16. Front Neurol 2023;14:1111906.
[28]	Ma W, Xia Y, Wu X, Yue Z, Cheng X, Fenster A, et al. Object-specific four-path network for stroke risk stratification of carotid arteries in ultrasound images. Comput Math Methods Med 2022;2022:2014349.
[29]	Zhu H, Jiang L, Zhang H, Luo L, Chen Y, Chen Y. An automatic machine learning approach for ischemic stroke onset time identification based on DWI and FLAIR imaging. Neuroimage Clin 2021; 31:102744.
[30]	Soltanpour M, Greiner R, Boulanger P, Buck B. Improvement of automatic ischemic stroke lesion segmentation in CT perfusion maps using a learned deep neural network. Comput Biol Med 2021;137:104849.
[31]	Zhang Y, Wu J, Liu Y, Chen Y, Wu EX, Tang X. MI-UNet: Multi-inputs UNet incorporating brain parcellation for stroke lesion segmentation from T1-weighted magnetic resonance images. IEEE J Biomed Health Inform 2021;25:526-535.
[32]	Tomita N, Jiang S, Maeder ME, Hassanpour S. Automatic post-stroke lesion segmentation on MR images using 3D residual convolutional neural network. Neuroimage Clin 2020;27:102276.