Computational intelligence for clinical diagnosis

Computational intelligence for clinical diagnosis / Ferdin Joe John Joseph, Valentina Emilia Balas, S. Suman Rajest, R. Regin, editors.

This book contains multidisciplinary advancements in healthcare and technology through artificial intelligence (AI). The topics are crafted in such a way to cover all the areas of healthcare that require AI for further development. Some of the topics that contain algorithms and techniques are explai...

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Bibliographic Details
Other Authors: Joseph, Ferdin Joe John (Editor), Balas, Valentina Emilia (Editor), Rajest, S. Suman, 1988- (Editor), Regin, R, 1985- (Editor)
Format: eBook
Language:English
Published: Cham : Springer, [2023]
Series:EAI/Springer innovations in communication and computing.
Subjects:
Artificial intelligence > Medical applications.
Artificial intelligence > Medical applications
Online Access:Click for online access
Table of Contents:
  • Intro
  • Preface
  • Acknowledgments
  • Contents
  • About the Editors
  • Chapter 1: A Novel Approach for Multiclass Brain Tumour Classification in MR Images
  • 1.1 Introduction
  • 1.1.1 Pre-processing
  • 1.1.2 Feature Extraction
  • 1.1.3 Segmentation
  • 1.1.4 Post-processing
  • 1.2 Related Work
  • 1.2.1 Proposed Algorithm
  • 1.2.2 Feature Extraction Methods
  • 1.2.3 Gray-level Co-occurrence Matrix (GLCM) (Texture)
  • 1.2.4 Gabor Transform (Texture)
  • 1.2.5 Wavelet Transform (Texture)
  • 1.2.6 Support Vector Machine (SVM)
  • 1.3 One-against-one Approach
  • 1.3.1 Fuzzy C Means (FCM)
  • 1.3.2 Fuzzy C-Means Algorithm
  • 1.4 Simulation Results
  • 1.5 Conclusion and Future Work
  • References
  • Chapter 2: Chicken Swarm-Based Feature Selection with Optimal Deep Belief Network for Thyroid Cancer Detection and Classificat...
  • 2.1 Introduction
  • 2.2 Related Works
  • 2.3 The Proposed Thyroid Cancer Diagnosis Model
  • 2.3.1 Pre-processing
  • 2.3.2 Process Involved in CSOFS Technique
  • 2.3.3 DBN Classification
  • 2.4 Performance Evaluation
  • 2.5 Conclusion
  • References
  • Chapter 3: Efficient Method for the prediction of Thyroid Disease Classification Using Support Vector Machine and Logistic Reg...
  • 3.1 Introduction
  • 3.2 Literature Review
  • 3.3 Proposed Methodology
  • 3.3.1 Dataset Description
  • 3.3.2 Pre-processing
  • 3.3.3 Feature Selection
  • 3.3.4 Classification
  • 3.3.5 Support Vector Machine
  • 3.4 Results and Discussions
  • 3.5 Conclusion
  • References
  • Chapter 4: Optimization of Management Response Toward Airborne Infections
  • 4.1 Introduction
  • 4.2 Literature Review
  • 4.3 Background and Key Issues
  • 4.3.1 Proposed Conceptual Framework
  • 4.4 Conclusion
  • 4.4.1 Future Directions
  • References
  • Chapter 5: Adaptive Sailfish Optimization-Contrast Limited Adaptive Histogram Equalization (ASFO-CLAHE) for Hyperparameter Tun...
  • 5.1 Introduction
  • 5.2 Literature Survey
  • 5.3 Proposed Methodology
  • 5.3.1 Histogram Equalization
  • 5.3.2 Clipped Histogram Equalization
  • 5.3.3 Adaptive Sailfish Optimization (ASFO)-CLAHE Algorithm
  • 5.4 Performance Measures
  • 5.5 Performance Measures
  • 5.6 Conclusion and Future Work
  • References
  • Chapter 6: Efficient Method for Predicting Thyroid Disease Classification using Convolutional Neural Network with Support Vect...
  • 6.1 Introduction
  • 6.2 Literature Review
  • 6.3 Materials and Methods
  • 6.3.1 Dataset Description
  • 6.3.2 Convolutional Neural Network
  • 6.3.3 Support Vector Machine
  • 6.4 Results and Discussions
  • 6.5 Conclusion
  • References
  • Chapter 7: Deep Learning in Healthcare Informatics
  • 7.1 Introduction
  • 7.1.1 Healthcare Informatics
  • 7.1.2 History
  • 7.1.3 Need of Healthcare Informatics
  • 7.1.4 Growth of Health Informatics

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