Artificial intelligence and image procesing in medical imaging /

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 i...

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Bibliographic Details
Corporate Author:	ScienceDirect (Online service)
Other Authors:	Zgallai, Walid A. (Editor), Ozsahin, Dilber Uzun (Editor)
Format:	eBook
Language:	English
Published:	London, U.K. : Academic Press, [2024]
Series:	Developments in biomedical engineering and bioelectronics.
Subjects:	Diagnostic imaging > Technological innovations. Artificial intelligence > Medical applications. Image processing > Digital techniques > Data processing. Imagerie pour le diagnostic > Innovations. Intelligence artificielle > Applications en médecine. Traitement d'images > Techniques numériques > Informatique. Electronic books.
Online Access:	Connect to the full text of this electronic book

Table of Contents:

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
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
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
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
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