Каталог / ТЕХНІЧНІ НАУКИ / Системи автоматизації проектувальних робіт
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- Назва:
- Коэн Янив ИК-спектрография и томография тканей человека и их анализ методами машинного обучения
- Альтернативное название:
- Коен Янів ІЧ-спектрографія та томографія тканин людини та їх аналіз методами машинного навчання
- ВНЗ:
- Высшая Школа Экономики
- Короткий опис:
- Коэн Янив ИК-спектрография и томография тканей человека и их анализ методами машинного обучения
ОГЛАВЛЕНИЕ ДИССЕРТАЦИИ
кандидат наук Коэн Янив
Contents
LIST OF ABBREVIATIONS
ACKNOLEDGMENTS
INTRODUCTION
The Relevance of the Research
The Objectives and Goals of Dissertation
The Scientific Novelty of the Study
The Practical Significance
The Key Findings of the Thesis to Be Defended
Methodology used in Dissertation
Authenticity of the results
The author's personal contribution
Approbation of Research Results
The list of the published articles where the main scientific results of the thesis are reflected
The Structure of Dissertation
CHAPTER 1. IR TOMOGRAPHY
1.1 Overview
1.2 Current State of the Art
1.3 Theoretical Background of the IR Tomography and IR Spectroscopy of Cancerous and Anomalous Biological Structures Detection and Identification
1.4 Cooling and heating of cancerous structures
1.5 Diameter and depth of the tumor
1.6 Experimental Clinical Tests
1.7 In-Vitro thermal imaging by use of Laparoscopic procedure
1.8 Results and Discussion
1.9 Conclusion
CHAPTER 2. DEVICES AND INSTRUMENTS
2.1 Field and background
2.2 Basic principles of FTIR-ATR detection
2.3 Information yielded
2.4 The means of operation
2.5 Brief Description of Medical I.R.O.S
2.6 Flow chart and short explanation
2.7 DATA BASE AND CLOUD PRESENTATION AND DESCRIPTION
CHAPTER 3. FTIR-ATR DATA CLASSIFICATION
3.1 Problem description: Cancer Detection
3.2 Data preparation and pre-processing
3.3 Machine Learning approach for classification
3.4 Partial least square regression (PLSR) and Principal component regression (PCR)
3
3.5 Training, calibration and validation
3.6 PCR/PLSR Summary
3.7 Linear Discriminant Analysis (LDA)
3.8 Naive Bayes classifier (NBC)
3.9 Conclusions of Machine Learning classifiers
3.10 Spectral biomarkers for discrimination between Normal and Malignant cells
CHAPTER 4. ARTIFICIAL NEURAL NETWORK
4.1 ANN concept - BASIC DEFINITIONS
4.2 Biological Neuron
4.3 Artificial Neuron
4.4 Multi-layer feed forward network
4.5 Feedforward error back-propagation Network
4.6 Basic MLFF network configuration
4.7 Feed-forward ANN classifier design
4.8 Network training Algorithms
4.9 Preliminary practical results
4.10 Conclusions
CHAPTER 5. SUMMARY
CHAPTER 6. PRACTICAL APPLICATIONS
CONCLUSION
REFERENCES
ANNEX: BACKGROUND TO THE SUBJECT
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