Frontiers of Artificial Intelligence in Medical Imaging / Navid Razmjooy and V. Rajinikanth, editors.

Format:

Book

Contributor:

Razmjooy, Navid, editor.

Rajinikanth, V., editor.

Series:

IOP Ebooks Series

Language:

English

Subjects (All):

Artificial intelligence.

Diagnostic imaging.

Physical Description:

1 online resource (252 pages)

Edition:

First edition.

Place of Publication:

Bristol, England : IOP Publishing, [2022]

Summary:

This book is designed to consider the recent advancements in hospitals to diagnose various diseases accurately using AI-supported detection procedures. The book also includes several chapters on machine learning, convoluted neural networks, segmentation, and deep learning-assisted two-class and multi-class classification.

Contents:

Intro

Editor biographies

Navid Razmjooy

Venkatesan Rajinikanth

List of contributors

Outline placeholder

Ali Saud Al-Bimani

Noradin Ghadimi

Seifedine Kadry

Hong Lin

Suresh Manic

Rajesh Kannan

J Sivakumar

Uma Suresh

Chapter 1 Health informatics system

1.1 Introduction to health informatics

1.2 Traditional scheme

1.3 Recent advancements

1.4 Artificial intelligence schemes

1.5 Deep-learning schemes

1.6 The Internet of Medical Things in health informatics

1.7 Health-band-supported patient monitoring

1.8 Accurate disease diagnosis

1.9 Summary

References

Chapter 2 Medical-imaging-supported disease diagnosis

2.1 Introduction

2.2 Cancer prevention

2.3 Early detection

2.4 Internal organs and medical imaging

2.4.1 Lung abnormality examination

2.4.2 Colon/rectum abnormality examination

2.4.3 Liver abnormality examination

2.4.4 Breast abnormality examination

2.4.5 Skin cancer examination

2.4.6 Brain cancer examination

2.4.7 COVID-19 examination

2.5 Summary

Chapter 3 Traditional and AI-based data enhancement

3.1 Clinical image improvement practices

3.2 Significance of image enrichment

3.3 Common image improvement methods

3.3.1 Artifact elimination

3.3.2 Noise elimination

3.3.3 Contrast enhancement

3.3.4 Image edge detection

3.3.5 Restoration

3.3.6 Image smoothing

3.3.7 Saliency detection

3.3.8 Local binary pattern

3.3.9 Image thresholding

3.4 Summary

Chapter 4 Computer-aided-scheme for automatic classification of brain MRI slices into normal/Alzheimer's disease

4.1 Introduction

4.2 Related work

4.3 Methodology

4.3.1 Proposed AD detection scheme

4.3.2 Machine-learning scheme

4.3.3 Deep-learning scheme

4.3.4 Scheme with integrated features.

4.3.5 Data collection and pre-processing

4.3.6 Feature extraction and selection

4.3.7 Validation

4.4 Results and discussions

4.5 Conclusion

Conflict of interest

Chapter 5 Design of a system for melanoma diagnosis using image processing and hybrid optimization techniques

5.1 Introduction

5.1.1 Conception

5.2 Literature review

5.3 Materials and methods

5.3.1 Artificial neural networks

5.3.2 Concept

5.3.3 Mathematical modeling of an ANN

5.4 Meta-heuristics

5.5 Electromagnetic field optimization algorithm

5.6 Developed electromagnetic field optimization algorithm

5.7 Simulation results

5.7.1 Image acquisition

5.7.2 Pre-processing stage

5.7.3 Processing stage

5.7.4 Classification

5.8 Final evaluation

5.9 Conclusions

Chapter 6 Evaluation of COVID-19 lesion from CT scan slices: a study using entropy-based thresholding and DRLS segmentation

6.1 Introduction

6.2 Context

6.3 Methodology

6.3.1 COVID-19 database

6.3.2 Image conversion and pre-processing

6.3.3 Image thresholding

6.3.4 Distance regularized level set segmentation

6.3.5 Performance computation and validation

6.4 Results and discussions

6.5 Conclusion

Chapter 7 Automated classification of brain tumors into LGG/HGG using concatenated deep and handcrafted features

7.1 Introduction

7.2 Context

7.3 Methodology

7.3.1 Image databases

7.3.2 Handcrafted feature extraction

7.3.3 Deep feature extraction

7.3.4 Feature concatenation

7.3.5 Performance measure computation and validation

7.4 Results and discussion

7.5 Conclusion

Chapter 8 Detection of brain tumors in MRI slices using traditional features with AI scheme: a study

8.1 Introduction

8.2 Context

8.3 Methodology

8.3.1 Image data sets.

8.3.2 Pre-processing

8.3.3 Post-processing

8.3.4 Feature extraction

8.3.5 Classification

8.3.6 Performance evaluation

8.4 Results and discussion

8.5 Conclusion

Acknowledgment

Chapter 9 Framework to classify EEG signals into normal/schizophrenic classes with machine-learning scheme

9.1 Introduction

9.2 Related work

9.3 Methodology

9.3.1 Electroencephalogram database

9.3.2 EEG pre-processing

9.3.3 Feature selection

9.3.4 Classification

9.3.5 Validation

9.4 Results and discussion

9.5 Conclusion

Chapter 10 Computerized classification of multichannel EEG signals into normal/autistic classes using image-to-signal transformation

10.1 Introduction

10.2 Context

10.3 Problem formulation

10.4 Methodology

10.4.1 Electroencephalogram database

10.4.2 Signal-to-image conversion with continuous wavelet transform

10.4.3 Nonlinear feature extraction

10.4.4 Locality-sensitive discriminant-analysis-based data reduction

10.4.5 Classifier implementation

10.4.6 Performance measure and validation

10.5 Results and discussion

10.6 Conclusion

References.

Notes:

Description based on publisher supplied metadata and other sources.

Description based on print version record.

Includes bibliographical references.

ISBN:

0-7503-4570-5

OCLC:

1429722658