Bladder volume estimation based on USG images
The article explores deep learning models in urological diagnostics to measure urinary bladder volume from medical images. It addresses the shortcomings of traditional methods by introducing advanced imaging techniques for more objective and precise analysis. The research employs Convolutional Neura...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Polish Academy of Sciences
2024-11-01
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| Series: | International Journal of Electronics and Telecommunications |
| Subjects: | |
| Online Access: | https://journals.pan.pl/Content/133213/PDF/13-4641-Mosorov-sk.pdf |
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| _version_ | 1846150818742403072 |
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| author | Volodymyr Mosorov Daniel Baradziej Marta Chodyka |
| author_facet | Volodymyr Mosorov Daniel Baradziej Marta Chodyka |
| author_sort | Volodymyr Mosorov |
| collection | DOAJ |
| description | The article explores deep learning models in urological diagnostics to measure urinary bladder volume from medical images. It addresses the shortcomings of traditional methods by introducing advanced imaging techniques for more objective and precise analysis. The research employs Convolutional Neural Networks (CNNs) and the MONAI platform for image segmentation and analysis, using data from The Cancer Imaging Archive to focus on urological regions. Findings suggest these models enhance diagnostic accuracy but also highlight the need for further modifications to tailor them to specific medical data, underscoring machine learning’s significant role in accurate medical assessments for urology. |
| format | Article |
| id | doaj-art-4b1796d75d96422992f2a510600ad2d1 |
| institution | Kabale University |
| issn | 2081-8491 2300-1933 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | Polish Academy of Sciences |
| record_format | Article |
| series | International Journal of Electronics and Telecommunications |
| spelling | doaj-art-4b1796d75d96422992f2a510600ad2d12024-11-28T08:41:30ZengPolish Academy of SciencesInternational Journal of Electronics and Telecommunications2081-84912300-19332024-11-01vol. 70No 4879886https://doi.org/10.24425/ijet.2024.152073Bladder volume estimation based on USG imagesVolodymyr Mosorov0Daniel Baradziej1Marta Chodyka2Lodz University of Technology, PolandLodz University of Technology, PolandJohn Paul II University in Biala Podlaska, PolandThe article explores deep learning models in urological diagnostics to measure urinary bladder volume from medical images. It addresses the shortcomings of traditional methods by introducing advanced imaging techniques for more objective and precise analysis. The research employs Convolutional Neural Networks (CNNs) and the MONAI platform for image segmentation and analysis, using data from The Cancer Imaging Archive to focus on urological regions. Findings suggest these models enhance diagnostic accuracy but also highlight the need for further modifications to tailor them to specific medical data, underscoring machine learning’s significant role in accurate medical assessments for urology.https://journals.pan.pl/Content/133213/PDF/13-4641-Mosorov-sk.pdfdeep learningbladder volume estimationmedical imaging convolutional neural networksimage segmentationmonai platformdiagnostic accuracy |
| spellingShingle | Volodymyr Mosorov Daniel Baradziej Marta Chodyka Bladder volume estimation based on USG images International Journal of Electronics and Telecommunications deep learning bladder volume estimation medical imaging convolutional neural networks image segmentation monai platform diagnostic accuracy |
| title | Bladder volume estimation based on USG images |
| title_full | Bladder volume estimation based on USG images |
| title_fullStr | Bladder volume estimation based on USG images |
| title_full_unstemmed | Bladder volume estimation based on USG images |
| title_short | Bladder volume estimation based on USG images |
| title_sort | bladder volume estimation based on usg images |
| topic | deep learning bladder volume estimation medical imaging convolutional neural networks image segmentation monai platform diagnostic accuracy |
| url | https://journals.pan.pl/Content/133213/PDF/13-4641-Mosorov-sk.pdf |
| work_keys_str_mv | AT volodymyrmosorov bladdervolumeestimationbasedonusgimages AT danielbaradziej bladdervolumeestimationbasedonusgimages AT martachodyka bladdervolumeestimationbasedonusgimages |