TIRADS Based Thyroid Nodule Classification Using Texture Exploiting Descriptors
General Information:
Level |
MASTER |
Title |
TIRADS Based Thyroid Nodule Classification Using Texture Exploiting Descriptors |
Specialty |
BIOMEDICAL ENGINEERING |
Cover Page:
Outline:
PREFACE
INTRODUCTION
CHAPTER 1
COMPUTER-AIDED DIAGNOSIS SYSTEMS (CADS).
Introduction
Decision and decision support
Stakeholders in the Decision Support Process
Typology of decisions.
Medical decision support.
The decision process
Decision models
The clinical reasoning
The decision in situation
The medical decision
Computer-Aided Diagnosis systems (CADs)
Definitions
The objectives of CADS
The main functions of CADs
Structure of a CADS.
1.4.5 Typology of the CADs.
Components of CADS
Knowledge models
Knowledge representation formalisms.
Methods of reasoning
Medical decision support methodologies: Numerical approach:
Discriminant analysis and statistical methods Bayesian systems.
The artificial intelligence approaches.
CHAPTER 2.
STATE OF THE ART
Introduction:.
Ti-rads
American College of Radiology (ACR)-TI-RADS
European (EU)-TI-RADS
Korean (K)-TI-RADS
Comparison between the different US classifications for thyroid nodules.
Related Work To Our Sutdy
CHAPTER 3.
PROPOSED METHODS
3.1 Pre-processing and Enhancement
3.1.1 Image Binarization
Image Normalization
Image Enhancement.
Feature Selection
Pass Band – Discrete Cosine Transform
Minority Oversampling.
CHAPTER 4
EXPERIMENTAL RESULTS
Dataset and Experimental Setup
Criteria for Classification Performance.
Result and Comparison of stage 1
Results of Stage 2
CONCLUSION AND FUTURE WORK
BIBLIOGRAPHY
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