Identification of Lung Nodules in Computed Tomography Lung Images Using Artificial Neural Networks
- Indbinding:
- Paperback
- Sideantal:
- 220
- Udgivet:
- 4. februar 2023
- Størrelse:
- 152x12x229 mm.
- Vægt:
- 326 g.
- 8-11 hverdage.
- 11. december 2024
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Forlænget returret til d. 31. januar 2025
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Ingen binding og kan opsiges når som helst.
Beskrivelse af Identification of Lung Nodules in Computed Tomography Lung Images Using Artificial Neural Networks
Benita K.J. Veronica provides a thorough review of employing artificial neural networks to identify lung nodules in computed tomography (CT) lung pictures in her article, "Identification of Lung Nodules in Computed Tomography Lung Images Using Artificial Neural Networks." Medical experts, academics, and researchers working in the fields of artificial intelligence and medical imaging will find this book to be an invaluable resource.
The author explores the numerous methods for spotting lung nodules in CT scans as well as the difficulties involved. The notion of artificial neural networks and their uses in medical imaging are then covered in the book. The author gives a thorough overview of the design, training, and testing of neural networks with a focus on the identification of lung nodules. The author's experiments produced clear and straightforward results, which make it simple for readers to comprehend the conclusions.
For individuals interested in learning more about the application of artificial neural networks in medical imaging, particularly in the recognition of lung nodules in CT scans, this book will be an invaluable resource. This book is a requirement-read for anyone interested in the subject because of the author's thorough study and pragmatic approach to the subject.
Medical professionals, researchers, students, artificial neural networks, computed tomography lung images, lung nodules, identification, medical imaging, CT images, Veronica K.J. Benita
The author explores the numerous methods for spotting lung nodules in CT scans as well as the difficulties involved. The notion of artificial neural networks and their uses in medical imaging are then covered in the book. The author gives a thorough overview of the design, training, and testing of neural networks with a focus on the identification of lung nodules. The author's experiments produced clear and straightforward results, which make it simple for readers to comprehend the conclusions.
For individuals interested in learning more about the application of artificial neural networks in medical imaging, particularly in the recognition of lung nodules in CT scans, this book will be an invaluable resource. This book is a requirement-read for anyone interested in the subject because of the author's thorough study and pragmatic approach to the subject.
Medical professionals, researchers, students, artificial neural networks, computed tomography lung images, lung nodules, identification, medical imaging, CT images, Veronica K.J. Benita
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