Deep learning in medical image processing and analysis:
Medical images, in various formats, are used by clinicians to identify abnormalities or markers associated with certain conditions, such as cancers, diseases, abnormalities or other adverse health conditions. Deep learning algorithms use vast volumes of data to train the computer to recognise certai...
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| Other Authors: | , , , |
|---|---|
| Format: | Electronic eBook |
| Language: | English |
| Published: |
Stevenage
The Institution of Engineering and Technology
2023
|
| Series: | Healthcare technologies series
59 |
| Online Access: | TUM01 UBY01 Volltext |
| Summary: | Medical images, in various formats, are used by clinicians to identify abnormalities or markers associated with certain conditions, such as cancers, diseases, abnormalities or other adverse health conditions. Deep learning algorithms use vast volumes of data to train the computer to recognise certain features in the images that are associated with the disease or condition that you wish to identify. Whilst analysing the images by eye can take a lot of time, deep learning algorithms have the benefit of reviewing medical images at a faster rate than a human can, which aids the clinician, speeding up diagnoses and freeing up clinicians' time for other duties. "Deep Learning in Medical Image Processing and Analysis" introduces the fundamentals of deep learning for biomedical image analysis for applications including ophthalmology, cancer detection and heart disease. The book considers the principles of multi-instance feature selection, swarm optimisation, parallel processing models, artificial neural networks, support vector machines, as well as their design and optimisation, in biomedical applications. Topics such as data security, patient confidentiality, effectiveness and reliability will also be discussed. Written by an international team of experts, this edited book covers principles and applications for industry and academic researchers, scientists, engineers, developers, and designers in the fields of machine learning, deep learning, AI, image processing, signal processing, computer science or related fields. It will also be of interest to standards bodies and regulators, and clinicians using deep learning models |
| Physical Description: | 1 Online-Ressource (xiv, 358 Seiten) Illustrationen, Diagramme |
| ISBN: | 9781839537943 |
| DOI: | 10.1049/PBHE059E |
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| 520 | |a Medical images, in various formats, are used by clinicians to identify abnormalities or markers associated with certain conditions, such as cancers, diseases, abnormalities or other adverse health conditions. Deep learning algorithms use vast volumes of data to train the computer to recognise certain features in the images that are associated with the disease or condition that you wish to identify. Whilst analysing the images by eye can take a lot of time, deep learning algorithms have the benefit of reviewing medical images at a faster rate than a human can, which aids the clinician, speeding up diagnoses and freeing up clinicians' time for other duties. "Deep Learning in Medical Image Processing and Analysis" introduces the fundamentals of deep learning for biomedical image analysis for applications including ophthalmology, cancer detection and heart disease. The book considers the principles of multi-instance feature selection, swarm optimisation, parallel processing models, artificial neural networks, support vector machines, as well as their design and optimisation, in biomedical applications. Topics such as data security, patient confidentiality, effectiveness and reliability will also be discussed. Written by an international team of experts, this edited book covers principles and applications for industry and academic researchers, scientists, engineers, developers, and designers in the fields of machine learning, deep learning, AI, image processing, signal processing, computer science or related fields. It will also be of interest to standards bodies and regulators, and clinicians using deep learning models | ||
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| isbn | 9781839537943 |
| language | English |
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| publisher | The Institution of Engineering and Technology |
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| series2 | Healthcare technologies series |
| spelling | Deep learning in medical image processing and analysis edited by Khaled Rabie, Chandran Karthik, Subrata Chowdhury and Pushan Kumar Dutta Stevenage The Institution of Engineering and Technology 2023 1 Online-Ressource (xiv, 358 Seiten) Illustrationen, Diagramme txt rdacontent c rdamedia cr rdacarrier Healthcare technologies series 59 Medical images, in various formats, are used by clinicians to identify abnormalities or markers associated with certain conditions, such as cancers, diseases, abnormalities or other adverse health conditions. Deep learning algorithms use vast volumes of data to train the computer to recognise certain features in the images that are associated with the disease or condition that you wish to identify. Whilst analysing the images by eye can take a lot of time, deep learning algorithms have the benefit of reviewing medical images at a faster rate than a human can, which aids the clinician, speeding up diagnoses and freeing up clinicians' time for other duties. "Deep Learning in Medical Image Processing and Analysis" introduces the fundamentals of deep learning for biomedical image analysis for applications including ophthalmology, cancer detection and heart disease. The book considers the principles of multi-instance feature selection, swarm optimisation, parallel processing models, artificial neural networks, support vector machines, as well as their design and optimisation, in biomedical applications. Topics such as data security, patient confidentiality, effectiveness and reliability will also be discussed. Written by an international team of experts, this edited book covers principles and applications for industry and academic researchers, scientists, engineers, developers, and designers in the fields of machine learning, deep learning, AI, image processing, signal processing, computer science or related fields. It will also be of interest to standards bodies and regulators, and clinicians using deep learning models Rabie, Khaled edt Karthik, Chandran edt Chowdhury, Subrata 1936- (DE-588)126057559 edt Dutta, Pushan Kumar edt Erscheint auch als Druck-Ausgabe 9781839537936 https://doi.org/10.1049/PBHE059E Verlag URL des Erstveröffentlichers Volltext |
| spellingShingle | Deep learning in medical image processing and analysis |
| title | Deep learning in medical image processing and analysis |
| title_auth | Deep learning in medical image processing and analysis |
| title_exact_search | Deep learning in medical image processing and analysis |
| title_exact_search_txtP | Deep learning in medical image processing and analysis |
| title_full | Deep learning in medical image processing and analysis edited by Khaled Rabie, Chandran Karthik, Subrata Chowdhury and Pushan Kumar Dutta |
| title_fullStr | Deep learning in medical image processing and analysis edited by Khaled Rabie, Chandran Karthik, Subrata Chowdhury and Pushan Kumar Dutta |
| title_full_unstemmed | Deep learning in medical image processing and analysis edited by Khaled Rabie, Chandran Karthik, Subrata Chowdhury and Pushan Kumar Dutta |
| title_short | Deep learning in medical image processing and analysis |
| title_sort | deep learning in medical image processing and analysis |
| url | https://doi.org/10.1049/PBHE059E |
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