Stroke Lesion Prediction by Bille-Viper-Segmentation with Tandem-MU-net Model
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 outcom...
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Editorial Office of Advanced Ultrasound in Diagnosis and Therapy
2025-03-01
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| Series: | Advanced Ultrasound in Diagnosis and Therapy |
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| Online Access: | https://www.journaladvancedultrasound.com/fileup/2576-2516/PDF/1738998955709-595530622.pdf |
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| author | Beevi Fathima, N Santhi Dr, N Ramasamy Dr |
| author_facet | Beevi Fathima, N Santhi Dr, N Ramasamy Dr |
| author_sort | Beevi Fathima, N Santhi Dr, N Ramasamy Dr |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-58c2662c45ea418c896d03d3cfff0165 |
| institution | Kabale University |
| issn | 2576-2516 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | Editorial Office of Advanced Ultrasound in Diagnosis and Therapy |
| record_format | Article |
| series | Advanced Ultrasound in Diagnosis and Therapy |
| spelling | doaj-art-58c2662c45ea418c896d03d3cfff01652025-02-12T05:45:03ZengEditorial Office of Advanced Ultrasound in Diagnosis and TherapyAdvanced Ultrasound in Diagnosis and Therapy2576-25162025-03-0191657810.37015/AUDT.2025.240011Stroke Lesion Prediction by Bille-Viper-Segmentation with Tandem-MU-net ModelBeevi Fathima, N Santhi Dr, N Ramasamy Dr0Noorul Islam Center for Higher Education Kumaracoil, ThuckalayStroke 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.https://www.journaladvancedultrasound.com/fileup/2576-2516/PDF/1738998955709-595530622.pdf|segmentation|tissue damage|fogging phase|infarcted area|stroke lesion|feature extraction |
| spellingShingle | 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 |segmentation|tissue damage|fogging phase|infarcted area|stroke lesion|feature extraction |
| title | Stroke Lesion Prediction by Bille-Viper-Segmentation with Tandem-MU-net Model |
| title_full | Stroke Lesion Prediction by Bille-Viper-Segmentation with Tandem-MU-net Model |
| title_fullStr | Stroke Lesion Prediction by Bille-Viper-Segmentation with Tandem-MU-net Model |
| title_full_unstemmed | Stroke Lesion Prediction by Bille-Viper-Segmentation with Tandem-MU-net Model |
| title_short | Stroke Lesion Prediction by Bille-Viper-Segmentation with Tandem-MU-net Model |
| title_sort | stroke lesion prediction by bille viper segmentation with tandem mu net model |
| topic | |segmentation|tissue damage|fogging phase|infarcted area|stroke lesion|feature extraction |
| url | https://www.journaladvancedultrasound.com/fileup/2576-2516/PDF/1738998955709-595530622.pdf |
| work_keys_str_mv | AT beevifathimansanthidrnramasamydr strokelesionpredictionbybillevipersegmentationwithtandemmunetmodel |