Kid-ML: ML For Kidney Malignant Tissues Identification Article Swipe
A considerable worldwide medical and health burden is imposed bykidney disease due to its high rates of morbidity and death as well as its higheconomic cost. Imaging tests can be used by doctors to detect kidney tumors orother diseases. Imaging studies include Magnetic Resonance Imaging(MRI),Computed Tomography(CT) scan, and ultrasound scan which consume a lot oftime from doctors to detect kidney cancers through them. In order to help doctorsto identify tumors in their early stages, they can use simple MachineLearning(ML) techniques or Deep Learning techniques through diagnostics andpredictions applications. A rise in interest in deep learning algorithms, which areArtificially Intelligently (AI) based, on a worldwide scale has enabled recentimprovements in medical imaging and kidney segmentation. Deep Learningtechniques which are AI-based can offer and identify the kidney tumor in a moreefficient method, allowing for the development of a more effective kidney tumordetection system. An input layer, one or more hidden layers, and an output layer
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.21608/msaeng.2023.291932
- https://msaeng.journals.ekb.eg/article_291932_e7b7eea4c3466f3fdaae03d261090365.pdf
- OA Status
- bronze
- Cited By
- 1
- References
- 13
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4361009209
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4361009209Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.21608/msaeng.2023.291932Digital Object Identifier
- Title
-
Kid-ML: ML For Kidney Malignant Tissues IdentificationWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-03-01Full publication date if available
- Authors
-
A. HassaneinList of authors in order
- Landing page
-
https://doi.org/10.21608/msaeng.2023.291932Publisher landing page
- PDF URL
-
https://msaeng.journals.ekb.eg/article_291932_e7b7eea4c3466f3fdaae03d261090365.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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bronzeOpen access status per OpenAlex
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https://msaeng.journals.ekb.eg/article_291932_e7b7eea4c3466f3fdaae03d261090365.pdfDirect OA link when available
- Concepts
-
Magnetic resonance imaging, Deep learning, Kidney, Segmentation, Kidney disease, Medicine, Artificial intelligence, Medical imaging, Radiology, Computer science, Internal medicineTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
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2024: 1Per-year citation counts (last 5 years)
- References (count)
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13Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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