Deep Learning for Chest Radiographs
Idioma: inglés
Editorial: Elsevier Science & Technology, Academic Press, 2021
Serie: Libro 2 de 6 - Primers in Biomedical Imaging Devices and Systems
- Tapa blanda
- Nuevo
Librería: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, AlemaniaBuchWeltWeit Ludwig Meier e.K.
Vendedor de IberLibro desde 11 de enero de 2012
Condición: Nuevo
EUR 87,95
Cantidad disponible: 2 disponibles
Añadir al carritoDescripción del artículo del vendedor
This item is printed on demand - it takes 3-4 days longer - Neuware -Deep Learning for Chest Radiographs enumerates different strategies implemented by the authors for designing an efficient convolution neural network-based computer-aided classification (CAC) system for binary classification of chest radiographs into 'Normal' and 'Pneumonia.' Pneumonia is an infectious disease mostly caused by a bacteria or a virus. The prime targets of this infectious disease are children below the age of 5 and adults above the age of 65, mostly due to their poor immunity and lower rates of recovery. Globally, pneumonia has prevalent footprints and kills more children as compared to any other immunity-based disease, causing up to 15% of child deaths per year, especially in developing countries. Out of all the available imaging modalities, such as computed tomography, radiography or X-ray, magnetic resonance imaging, ultrasound, and so on, chest radiographs are most widely used for differential diagnosis between Normal and Pneumonia. In the CAC system designs implemented in this book, a total of 200 chest radiograph images consisting of 100 Normal images and 100 Pneumonia images have been used. These chest radiographs are augmented using geometric transformations, such as rotation, translation, and flipping, to increase the size of the dataset for efficient training of the Convolutional Neural Networks (CNNs). A total of 12 experiments were conducted for the binary classification of chest radiographs into Normal and Pneumonia. It also includes in-depth implementation strategies of exhaustive experimentation carried out using transfer learning-based approaches with decision fusion, deep feature extraction, feature selection, feature dimensionality reduction, and machine learning-based classifiers for implementation of end-to-end CNN-based CAC system designs, lightweight CNN-based CAC system designs, and hybrid CAC system designs for chest radiographs. This book is a valuable resource for academicians, researchers, clinicians, postgraduate and graduate students in medical imaging, CAC, computer-aided diagnosis, computer science and engineering, electrical and electronics engineering, biomedical engineering, bioinformatics, bioengineering, and professionals from the IT industry. Englisch.…
N° de ref. del artículo 9780323901840
- Título
- Deep Learning for Chest Radiographs
- Autor
- Yashvi Chandola
- Editorial
- Elsevier Science & Technology, Academic Press
- Año de publicación
- 2021
- Estado
- Neu
- Encuadernación
- Taschenbuch
- Idioma
- inglés
- ISBN 10
- 0323901840
- ISBN 13
- 9780323901840
- Peso del artículo
- 500 gramos
- Dimensiones
- 235x191x10 mm
- Serie
- Libro 2 de 6: Primers in Biomedical Imaging Devices and Systems
Deep Learning for Chest Radiographs enumerates different strategies implemented by the authors for designing an efficient convolution neural network-based computer-aided classification (CAC) system for binary classification of chest radiographs into "Normal" and "Pneumonia." Pneumonia is an infectious disease mostly caused by a bacteria or a virus. The prime targets of this infectious disease are children below the age of 5 and adults above the age of 65, mostly due to their poor immunity and lower rates of recovery. Globally, pneumonia has prevalent footprints and kills more children as compared to any other immunity-based disease, causing up to 15% of child deaths per year, especially in developing countries. Out of all the available imaging modalities, such as computed tomography, radiography or X-ray, magnetic resonance imaging, ultrasound, and so on, chest radiographs are most widely used for differential diagnosis between Normal and Pneumonia. In the CAC system designs implemented in this book, a total of 200 chest radiograph images consisting of 100 Normal images and 100 Pneumonia images have been used. These chest radiographs are augmented using geometric transformations, such as rotation, translation, and flipping, to increase the size of the dataset for efficient training of the Convolutional Neural Networks (CNNs). A total of 12 experiments were conducted for the binary classification of chest radiographs into Normal and Pneumonia. It also includes in-depth implementation strategies of exhaustive experimentation carried out using transfer learning-based approaches with decision fusion, deep feature extraction, feature selection, feature dimensionality reduction, and machine learning-based classifiers for implementation of end-to-end CNN-based CAC system designs, lightweight CNN-based CAC system designs, and hybrid CAC system designs for chest radiographs.
This book is a valuable resource for academicians, researchers, clinicians, postgraduate and graduate students in medical imaging, CAC, computer-aided diagnosis, computer science and engineering, electrical and electronics engineering, biomedical engineering, bioinformatics, bioengineering, and professionals from the IT industry.
- Provides insights into the theory, algorithms, implementation, and application of deep-learning techniques for medical images such as transfer learning using pretrained CNNs, series networks, directed acyclic graph networks, lightweight CNN models, deep feature extraction, and conventional machine learning approaches for feature selection, feature dimensionality reduction, and classification using support vector machine, neuro-fuzzy classifiers
- Covers the various augmentation techniques that can be used with medical images and the CNN-based CAC system designs for binary classification of medical images focusing on chest radiographs
- Investigates the development of an optimal CAC system design with deep feature extraction and classification of chest radiographs by comparing the performance of 12 different CAC system designs
“Sinopsis” puede pertenecer a otra edición de este título.
Acerca del autor
Jitendra Virmani received his B-Tech (Hons.) in Instrumentation Engineering from Sant Longowal Institute of Engineering and Technology, Punjab in 1999 and M-Tech in Electrical Engineering with specialization in Measurement and Instrumentation from the Indian Institute of Technology, Roorkee in 2006. He served in the academia for nine years before joining the PhD programme in 2009 at Biomedical Instrumentation Laboratory, Electrical Engineering Department, Indian Institute of Technology, Roorkee as a full time Research Scholar under MHRD Assistantship. He received his PhD from the Indian Institute of Technology, Roorkee in 2013. After his PhD he served in Academia for Jaypee University of Information Technology, Solan, Himachal Pradesh and Thapar Institute of Engineering and Technology, Patiala, Punjab before joining CSIR-CSIO, Chandigarh in August 2016. He is presently working at CSIR-CSIO, Chandigarh. He is a Life member of the Institute of Engineers (IEI), India and Computer Society of India. He has published 85 papers in various journals, conferences and Book chapters with various reputed publishers. He has delivered more than 35 expert talks on various platforms basically on application of machine learning and deep learning algorithms for medical images. He is Editorial Board Member of International Journal of Image Mining published by Inderscience Publishers. His research interests include application of machine learning and deep learning algorithms for analysis of medical images.
H.S Bhadauria received his B-Tech in Computer Science and Engineering from Aligarh Muslim University, Aligarh in 1999, and M-Tech in Electronics Engineering from Aligarh Muslim University, Aligarh in 2004. He received his PhD on Detection and Segmentation of Brain Hemorrhage using CT images from Biomedical Signal and Image Processing Laboratory, Indian Institute of Technology - Roorkee in 2013. During his PhD he worked on enhancing the detection and segmentation of brain hemorrhage using CT imaging modality. He has served in academia for more than 12 years. He is presently serving as a Professor in the Department of Computer Science & Engineering at Govind Ballabh Pant Institute of Engineering and Technology, Pauri Garhwal, Uttarakhand. He is a life member of Institute of Engineers (IEI), India. He has published more than 60 research papers in International and National Journals and Conferences. His areas of research interest are Digital Image and Digital Signal Processing.
Papendra Kumar received his B.E in Computer Science and Engineering from Govind Ballabh Pant Institute of Engineering and Technology, Pauri Garhwal, Uttarakhand in 2007 and M-Tech in Digital Signal Processing from Govind Ballabh Pant Institute of Engineering and Technology, Pauri Garhwal, Uttarakhand in 2011. He is presently serving as an Assistant Professor in the Department of Computer Science & Engineering at Govind Ballabh Pant Institute of Engineering and Technology, Pauri Garhwal, Uttarakhand. His areas of research interest are Digital Image and Digital Signal Processing.
“Acerca de” puede pertenecer a otra edición de este título.
BuchWeltWeit Ludwig Meier e.K.
Bergisch Gladbach, Alemania
Vendedor de IberLibro desde 11 de enero de 2012
Tarifas de envío de Alemania a Estados Unidos de America
| Artículo | De 5 a 15 días hábiles | De 5 a 15 días hábiles |
|---|---|---|
| Primer artículo | EUR 23,00 | EUR 23,00 |
Métodos de pago
- Cheque
- Giro bancario
- PayPal
Información empresarial del vendedor
BuchWeltWeit Ludwig Meier e.K.
Alemania
Condiciones de venta
BuchWeltWeit Inh. Ludwig Meier e.K.
Inhaber: Ludwig Meier
De-Gasperi-Str. 8
51469 Bergisch Gladbach
Deutschland
Tel.: +49(0)22028659300
Fax: +49(0)22028659301
E-Mail: info@buchweltweit.de
Registergericht: Amtsgericht Köln
Registernummer: HRA 28253
Umsatzsteuer-Identifikationsnummer: DE815259907
Wir sind zur Teilnahme an einem Streitbeilegungsverfahren vor einer Verbraucherschlichtungsstelle weder verpflichtet noch bereit.
Derecho al desistimiento
Si es un consumidor, puede rescindir el contrato de acuerdo con lo siguiente. Por consumidor se entiende cualquier persona física que actúe con fines ajenos a su actividad comercial, empresarial, oficio o profesión.
Información sobre el derecho de desistimiento
Derecho legal de desistimiento
Tiene derecho a rescindir este contrato en un plazo de 14 días sin dar ningún motivo.
El periodo de desistimiento vencerá a los 14 días desde que usted, o un tercero que no sea el transportista e indicado por usted, adquiera la posesión física del último bien o del último lote o pieza.
Para ejercer el derecho de desistimiento, complete de forma electrónica y envíe una declaración clara en nuestro sitio web, desde "Mis compras" en "Mi cuenta". Le enviaremos sin demora un acuse de recibo de dicho desistimiento a través de un soporte duradero (por ejemplo, por correo electrónico).
Para cumplir con el plazo de desistimiento, basta con que envíe su comunicación relativa al ejercicio del derecho de desistimiento antes de que venza el periodo de desistimiento.
Efectos del desistimiento
Si rescinde este contrato, le reembolsaremos todos los pagos que hayamos recibido de usted, incluidos los gastos de envío (excepto los gastos adicionales que surjan si elige un tipo de envío que no sea el tipo de envío estándar más económico que ofrecemos).
Podemos hacer una deducción del reembolso por la pérdida de valor de cualquier bien suministrado, si la pérdida es el resultado de una manipulación innecesaria por su parte.
Efectuaremos el reembolso sin demoras indebidas y, a más tardar, 14 días después de que se nos informe de su decisión de rescindir este contrato.
Efectuaremos el reembolso utilizando el mismo medio de pago que utilizó para la transacción inicial, a menos que haya acordado expresamente lo contrario; en cualquier caso, no incurrirá en ningún cargo como resultado de dicho reembolso.
Podremos retener el reembolso hasta que hayamos recibido los bienes o hasta que nos haya presentado una prueba de que los ha devuelto, lo que ocurra primero.
Deberá devolver los bienes o entregarlos a BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany, sin demoras indebidas y, en cualquier caso, en un plazo máximo de 14 días a partir del día en que nos comunique su desistimiento del presente contrato. El plazo se cumple si devuelve la mercancía antes de que venza el periodo de 14 días. Tendrá que asumir los gastos directos de devolución de los bienes. Usted solo es responsable de la disminución del valor de los bienes como resultado de una manipulación distinta a la necesaria para establecer la naturaleza, las características y el funcionamiento de los bienes.
Excepciones al derecho de desistimiento
El derecho de desistimiento no se aplica a lo siguiente:
- La entrega de periódicos, diarios o revistas, con la excepción de los contratos de suscripción; y
- El suministro de contenido digital que no se proporcione en un soporte tangible (por ejemplo, en un CD o DVD) si, al hacer el pedido, aceptó que podíamos empezar a entregarlo y que no podría desistir una vez iniciada la entrega.
Condiciones de envío
Der Versand ins Ausland findet IMMER mit DHL statt. Auch nach Österreich verschicken wir nur mit DHL! Daher Standardversand == Luftpost!