Deep Learning for Chest Radiographs | Computer-Aided Classification
Idioma: inglés
Editorial: Elsevier Inc, 2021
Serie: Libro 2 de 6 - Primers in Biomedical Imaging Devices and Systems
- Tapa blanda
- Nuevo
Librería: preigu, Osnabrück, Alemaniapreigu
Vendedor de IberLibro desde 5 de agosto de 2024
Condición: Nuevo
EUR 83,25
Cantidad disponible: 5 disponibles
Añadir al carritoDescripción del artículo del vendedor
Deep Learning for Chest Radiographs | Computer-Aided Classification | Yashvi Chandola (u. a.) | Taschenbuch | Einband - flex.(Paperback) | Englisch | 2021 | Elsevier Inc | EAN 9780323901840 | Verantwortliche Person für die EU: Elsevier B.V., Radarweg 29, 1043 NX AMSTERDAM, NIEDERLANDE, productsafety[at]elsevier[dot]com | Anbieter: preigu.
N° de ref. del artículo 119588886
- Título
- Deep Learning for Chest Radiographs | Computer-Aided Classification
- Autor
- Yashvi Chandola (u. a.)
- Editorial
- Elsevier Inc
- Año de publicación
- 2021
- Estado
- Neu
- Encuadernación
- Taschenbuch
- Idioma
- inglés
- ISBN 10
- 0323901840
- ISBN 13
- 9780323901840
- Peso del artículo
- 401 gramos
- Dimensiones
- 235 x 191 x 12 mm
- Serie
- Libro 2 de 6: Primers in Biomedical Imaging Devices and Systems
- Catálogos de vendedores
- Bücher
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.
preigu
Osnabrück, Alemania
Vendedor de IberLibro desde 5 de agosto de 2024
Tarifas de envío de Alemania a Estados Unidos de America
| Artículo | De 60 a 60 días hábiles | De 60 a 60 días hábiles |
|---|---|---|
| Primer artículo | EUR 70,00 | EUR 70,00 |
Métodos de pago
- PayPal
Descripción de la tienda
preigu betreibt einen Onlineversandhandel mit über 1 Mio. Produkten in verschiedenen Sortimenten. Das Kernsortiment besteht aus Büchern, Medien und Spielwaren. Ein gelungenes Einkaufserlebnis ist das Ziel einer jeden Bestellung bei preigu, denn der Kunde und seine Zufriedenheit stehen an erster Stelle. preigu setzt daher auf einen kompetenten Kundenservice, funktionierende Prozesse und schnelle Reaktion.
Especialidad
Bücher, SpielwarenInformación empresarial del vendedor
preigu GmbH & Co. KG
Lengericher Landstraße 19
Osnabrück, Alemania 49078
Condiciones de venta
About Us
Legal website operator identification:
preigu GmbH & Co. KG
Lengericher Landstr. 19
49078 Osnabrück
Germany
Telephone: +49 (0) 541 / 580 72 84
Email: mail@preigu.de
VAT No: DE 455 380 498
AG Osnabrück - HRA 209647
PhG: preigu Verwaltung GmbH
AG Osnabrück - HRB 221793
CEO: Ansas Meyer
We are neither willing nor obliged to participate in dispute resolution proceedings before consumer arbitration boards.
We are a member of the initiative "FairCommerce" since 30.11.2016.
For more information, see: https://www.haendlerbund.de/de/haendlerbund/interessenvertretung/faircommerce
Derecho al desistimiento
Instructions for revocation
Right of withdrawal for the sale of goods
Revocation right for consumers
(A ‘consumer' is any natural person who concludes a legal transaction which, to an overwhelming extent, cannot be attributed to either his commercial or independent professional activities.)
Instructions for revocation
Revocation right
You have the right to revoke this contract within 14 days without specifying any reasons.
The revocation period is 14 days with effect from the day,
-
on which you or a third party nominated by you, which is not the carrier, had taken possession of the products, provided you had ordered one or more products within the scope of a standard order and this/these product/products is/are delivered uniformly;
-
on which you or a third party nominated by you, which is not the carrier, had taken possession of the last product, provided you had ordered several products within the scope of a standard order and these products are delivered separately;
-
on which you or a third party nominated by you, which is not the carrier, had taken possession of the last part delivery or the last unit, provided you had ordered a product, which is delivered in several part deliveries or units;
To exercise your right of withdrawal, you must inform us (preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, Telephone number: +49 (0) 541 / 580 72 84, E-Mail address: mail@preigu.de) by means of a clear declaration (e.g. a letter sent by post, or an e-mail) of your decision to withdraw from this contract. You can use the attached model withdrawal form for this purpose, which is, however, not mandatory.
You can also exercise your right of withdrawal online by clicking on a button labelled accordingly (such as ‘Withdraw from contract' or similar) on the AbeBooks/ZVAB website. If you use this online function, you will immediately receive a confirmation of receipt on a durable medium (e.g. via email) containing information on the content of the withdrawal notice, as well as the date and time of its receipt.
In order to safeguard the revocation period, it is sufficient that you send the notification about the exercise of the revocation right before the expiry of the revocation period.
Consequences of the revocation
If you revoke this contract, we shall repay all the payments, which we received from you, including the delivery costs (with the exception of additional costs, which arise from that fact that you selected a form of delivery other than the most reasonable standard delivery offered by us), immediately and at the latest within 14 days from the day on which we received the notification about the revocation of this contract from you. We use the same means of payment, which you had originally used during the original transaction, for this repayment unless expressly agreed otherwise with you; you will not be charged any fees owing to this repayment.
We can refuse the repayment until the products are returned to us or until you have furnished evidence that you have sent the products back to us, depending on whichever is earlier.
You must return or transfer the products to us immediately and, in any case, at the latest within 14 days with effect from the day on which you inform us of the revocation of this contract. The deadline is maintained if you send the products before the expiry of the 14 day deadline.
You bear the direct costs for returning the products.
You must pay for any depreciation of the products only if this depreciation can be attributed to any handling with you that was not necessary for checking the condition, features and functionality of the products.
Criteria for exclusion or expiry
The revocation right is not available for contracts
-
for delivery of products, which are not prefabricated and for whose manufacturing an individual selection or stipulation by the consumer is important or which are clearly tailored to the personal requirements of the consumer;
-
for delivery of products, which can spoil quickly or whose use-by date would be exceeded quickly;
-
for delivery of alcoholic drinks, whose price was agreed at the time of concluding the contract, which however can be delivered 30 days after the conclusion of the contract at the earliest and whose current value depends on the fluctuations in the market, on which the entrepreneur has no influence;
-
for delivery of newspapers, periodicals or magazines with the exception of subscription contracts. The revocation right expires prematurely in case of contracts
-
for delivery of sealed products, which are not suitable for return for reasons of health protection or hygiene if their seal has been removed after the delivery;
-
for delivery of products if they have been mixed inseparably with other goods after the delivery, owing to their condition;
-
for delivery of sound or video recording or computer software in a sealed package if the seal has been removed after the delivery.
Specimen - revocation form
(If you wish to revoke the contract, please fill up this form and send it back to us.)
-
To preigu GmbH & Co. KG, Lengericher Landstr. 19, 49078 Osnabrück, Email address: mail@preigu.de :
-
I/we () herewith revoke the contract concluded by me/ us () regarding the purchase of the following products ()/
the provision of the following service () -
Ordered on ()/ received on ()
-
Name of the consumer(s)
-
Address of the consumer(s)
-
Signature of the consumer(s) (only in case of a notification on paper)
-
Date
(*) Cross out the incorrect option.