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| LEADER |
00000cam a2200000 a 4500 |
| 001 |
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| 005 |
20260327175327.9 |
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| 007 |
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| 008 |
240124s2024 enka ob 001 0 eng d |
| 040 |
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|a YDX
|b eng
|c YDX
|d UKAHL
|d OPELS
|d OCLCO
|d EMU
|d N$T
|d AU@
|d OCLKB
|d EBLCP
|d OCLCA
|d UKKRT
|d OCLCQ
|d OCLCL
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| 019 |
|
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|a 1418730993
|a 1420158814
|a 1422256323
|
| 020 |
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|a 9780323954631
|q (electronic bk.)
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| 020 |
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|a 0323954634
|q (electronic bk.)
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| 020 |
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|z 9780323954624
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| 020 |
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|z 0323954626
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|a (OCoLC)1418816758
|z (OCoLC)1418730993
|z (OCoLC)1420158814
|z (OCoLC)1422256323
|
| 050 |
|
4 |
|a RC78.7.D53
|
| 082 |
0 |
4 |
|a 616.0754028563
|2 23/eng/20240215
|
| 049 |
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|a TXAM
|
| 245 |
0 |
0 |
|a Artificial intelligence and image procesing in medical imaging /
|c edited by Walid A. Zgallai, Dilber Uzun Ozsahin.
|
| 260 |
|
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|a London, U.K. :
|b Academic Press,
|c [2024]
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| 300 |
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|a 1 online resource :
|b illustrations.
|
| 336 |
|
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|a text
|b txt
|2 rdacontent
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| 337 |
|
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|a computer
|b c
|2 rdamedia
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| 338 |
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|a online resource
|b cr
|2 rdacarrier
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| 490 |
1 |
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|a Developments in biomedical engineering and bioelectronics
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| 504 |
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|a Includes bibliographical references and index.
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| 520 |
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|a Deals with the applications of processing medical images with a view of improving the quality of the data in order to facilitate better decision- making. The book covers the basics of medical imaging and the fundamentals of image processing. It explains spatial and frequency domain applications of image processing, introduces image compression techniques and their applications, and covers image segmentation techniques and their applications. The book includes object detection and classification applications and provides an overall background to statistical analysis in biomedical systems. The role of machine learning, including neural networks, deep learning, and the implications of the expansion of artificial intelligence is also covered.
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| 588 |
0 |
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|a Print version record.
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| 505 |
0 |
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|a Front Cover -- Artificial Intelligence and Image Processing in Medical Imaging -- Copyright Page -- Contents -- List of contributors -- 1 Introduction to machine learning and artificial intelligence -- 1.1 Comprehensive introduction to machine learning and artificial intelligence -- 1.1.1 Types of machine learning -- 1.1.2 Machine learning algorithm -- 1.1.2.1 Support vector machine -- 1.1.2.2 Logistic regression -- 1.1.2.3 Linear regression -- 1.1.2.4 K-means clustering -- 1.1.2.5 K-nearest neighbor -- 1.1.2.6 Decision tree -- 1.1.2.7 Random forest -- 1.1.3 Deep learning
|
| 505 |
8 |
|
|a 1.1.4 Terminologies in machine learning -- 1.1.4.1 Bias and variance -- 1.1.4.2 Overfitting and underfitting -- 1.1.4.3 Principal component analysis -- 1.1.4.4 Cross-validation -- 1.1.4.5 Gradient descent -- 1.1.4.6 Cost function -- 1.1.4.7 Parameter and hyperparameter -- 1.1.4.8 Transfer learning -- 1.1.4.9 Performance evaluation matrix -- References -- 2 Convolution neural network and deep learning -- Abbreviations -- 2.1 Brief history of deep learning -- 2.2 Deep learning -- 2.2.1 Convolution neural network -- 2.2.1.1 The basic architecture of the convolutional neural network
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| 505 |
8 |
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|a 2.2.1.1.1 Convolutional layer -- 2.2.1.1.2 Pooling layer -- 2.2.1.1.3 Fully connected layer -- 2.2.2 Common convolutional neural network models -- 2.2.2.1 AlexNet -- 2.2.2.2 VGG16 -- 2.2.2.3 ResNet50 -- 2.2.2.4 InceptionV3 -- 2.2.2.5 EfficientNet -- 2.2.3 Applications of convolutional neural networks -- 2.3 Common terminologies in deep learning -- 2.3.1 Neural network -- 2.3.2 Recurrent neural network -- 2.3.3 Generative adversarial network -- 2.3.4 Back-propagation -- 2.3.5 Gradient descent -- 2.3.6 Activation function -- 2.3.7 Overfitting -- 2.3.8 Batch normalization -- 2.3.9 Transfer learning
|
| 505 |
8 |
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|a 2.3.10 Autoencoder -- 2.3.11 Restricted Boltzmann machine -- 2.3.12 Convolutional layer -- 2.3.13 Pooling layer -- 2.3.14 Fully connected layer -- 2.3.15 Embedding layer -- 2.3.16 Cross-entropy -- 2.3.17 Optimizer -- 2.3.18 Back-propagation through time -- 2.3.19 Batch size -- 2.3.20 Epoch -- 2.3.21 Vanishing gradient -- 2.3.22 Strides -- 2.3.23 Padding -- 2.3.24 Hyperparameter -- 2.3.25 Filters -- 2.3.26 Dropout -- References -- 3 Image preprocessing phase with artificial intelligence methods on medical images -- 3.1 Introduction -- 3.2 Medical imaging -- 3.3 Image processing
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| 505 |
8 |
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|a 3.4 Histogram equalization -- 3.5 Power-law transformation -- 3.6 Linear transformation -- 3.7 Log transformation -- 3.8 Mean filter -- 3.9 Median filter -- 3.10 Gaussian filter -- 3.11 Image compression -- 3.12 Image enhancement -- 3.13 Image resizing -- 3.14 Image restoration -- 3.15 Image segmentation -- 3.16 Artificial intelligence in medical imaging -- 3.17 Applications of image preprocessing in medical imaging -- 3.18 Simplified: applications of artificial intelligence and image preprocessing in medical imaging -- 3.19 Medical cases -- 3.19.1 Lung cancer-computed tomography scans
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| 650 |
|
0 |
|a Diagnostic imaging
|x Technological innovations.
|
| 650 |
|
0 |
|a Artificial intelligence
|x Medical applications.
|
| 650 |
|
0 |
|a Image processing
|x Digital techniques
|x Data processing.
|
| 650 |
|
6 |
|a Imagerie pour le diagnostic
|x Innovations.
|
| 650 |
|
6 |
|a Intelligence artificielle
|x Applications en médecine.
|
| 650 |
|
6 |
|a Traitement d'images
|x Techniques numériques
|x Informatique.
|
| 655 |
|
7 |
|a Electronic books.
|2 local
|
| 700 |
1 |
|
|a Zgallai, Walid A.,
|e editor.
|
| 700 |
1 |
|
|a Ozsahin, Dilber Uzun,
|e editor.
|
| 710 |
2 |
|
|a ScienceDirect (Online service)
|
| 776 |
0 |
8 |
|i Print version:
|z 9780323954631
|
| 776 |
0 |
8 |
|i Print version:
|z 0323954626
|z 9780323954624
|w (OCoLC)1370926185
|
| 776 |
0 |
8 |
|i Print version:
|t ARTIFICIAL INTELLIGENCE AND IMAGE PROCESSING IN MEDICAL IMAGING.
|d [S.l.] : ELSEVIER ACADEMIC PRESS, 2023
|z 0323954626
|w (OCoLC)1370926185
|
| 830 |
|
0 |
|a Developments in biomedical engineering and bioelectronics.
|
| 856 |
4 |
0 |
|u http://proxy.library.tamu.edu/login?url=https://www.sciencedirect.com/science/book/9780323954624
|z Connect to the full text of this electronic book
|t 0
|
| 955 |
|
|
|a Elsevier ScienceDirect 2026-2027
|
| 994 |
|
|
|a 92
|b TXA
|
| 999 |
f |
f |
|i e74d344b-3ffd-4224-b74f-4df6fd9f6e49
|s 3d22b3d9-ea78-43b0-bf08-01113be5ae45
|t 0
|
| 952 |
f |
f |
|a Texas A&M University
|b College Station
|c Electronic Resources
|s www_evans
|d Available Online
|t 0
|e RC78.7.D53
|h Library of Congress classification
|
| 998 |
f |
f |
|a RC78.7.D53
|t 0
|l Available Online
|