PRZEDMIOTEM OFERTY JEST KOD DOSTĘPOWY DO KSIĄŻKI ELEKTRONICZNEJ (EBOOK)
KSIĄŻKA JEST DOSTĘPNA NA ZEWNĘTRZNEJ PLATFORMIE. KSIĄŻKA NIE JEST W POSTACI PLIKU.
Cancer research is currently a vital field of study as it affects a wide range of the population either directly or indirectly. Breast and cervical cancer are two prevalent types that pose a threat to women’s health and wellness. Due to this, further research on the importance of medical informatics within this field is necessary to ensure patients receive the best possible attention and care. The Research Anthology on Medical Informatics in Breast and Cervical Cancer provides current research and information on how medical informatics are utilized within the field of breast and cervical cancer and considers the best practices and challenges of its implementation. Covering key topics such as women’s health, wellness, oncology, and patient care, this major reference work is ideal for medical professionals, nurses, oncologists, policymakers, researchers, academicians, scholars, practitioners, instructors, and students.
- Autorzy: Management Association Information Resources
- Wydawnictwo: IGI Global
- Data wydania: 2022
- Wydanie:
- Liczba stron:
- Forma publikacji: ePub (online)
- Język publikacji: angielski
- ISBN: 9781668471388
BRAK MOŻLIWOŚCI POBRANIA PLIKU. Drukowanie: OGRANICZENIE DO 2 stron. Kopiowanie: OGRANICZENIE DO 2 stron.
- Cover
- Title Page
- Copyright Page
- Editor-in-Chief
- Associate Editors
- Editorial Advisory Board
- Preface
- Section 1: Classification and Identification Techniques
- Chapter 1: Artificial Intelligence and Image Analysis for the Identification of Endometrial Malignancies
- ABSTRACT
- INTRODUCTION
- MATERIALS AND METHODS
- RESULTS
- DISCUSSION
- FUTURE RESEARCH DIRECTIONS AND STUDY LIMITATIONS
- CONCLUSION
- REFERENCES
- KEY TERMS AND DEFINITIONS
- Chapter 2: Algorithm Enhancements for Improvement of Localized Classification of Uterine Cervical Cancer Digital Histology Images
- ABSTRACT
- INTRODUCTION
- METHODS
- EXPERIMENTS AND RESULTS ANALYSIS
- CONCLUSION
- ACKNOWLEDGMENT
- REFERENCES
- Chapter 3: A Hybrid Deep Learning and Handcrafted Feature Approach for Cervical Cancer Digital Histology Image Classification
- ABSTRACT
- INTRODUCTION
- METHODS
- EXPERIMENTS PERFORMED AND RESULTS
- CONCLUSION
- ACKNOWLEDGMENT
- REFERENCES
- Chapter 4: Classification of Breast Lesions in Frontal Thermographic Images Using a Diagnosis Aid Intelligent System
- ABSTRACT
- INTRODUCTION
- BACKGROUND
- PROPOSED METHOD
- SOLUTIONS AND RECOMMENDATIONS
- CONCLUSION
- ACKNOWLEDGMENT
- REFERENCES
- Chapter 5: Feature Selection Based on Dialectical Optimization Algorithm for Breast Lesion Classification in Thermographic Images
- ABSTRACT
- INTRODUCTION
- BACKGROUND
- METHOD
- RESULTS AND DISCUSSION
- FUTURE RESEARCH DIRECTIONS
- CONCLUSION
- ACKNOWLEDGMENT
- REFERENCES
- Chapter 6: Mammogram Classification Using Nonsubsampled Contourlet Transform and Gray-Level Co-Occurrence Matrix
- ABSTRACT
- INTRODUCTION
- METHODOLOGY
- EXPERIMENTAL RESULTS AND ANALYSIS
- RESULTS AND DISCUSSIONS
- CONCLUSION
- REFERENCES
- Chapter 7: Split and Merge-Based Breast Cancer Segmentation and Classification
- ABSTRACT
- INTRODUCTION
- THE PROPOSED APPROACH
- CLASSIFICATION STEP
- EXPERIMENTAL RESULTS
- CONCLUSION
- REFERENCES
- Chapter 8: An Ensemble Feature Subset Selection for Women Breast Cancer Classification
- ABSTRACT
- 1. INTRODUCTION
- 2. RELATED WORK
- 3. ARTIFICIAL NEURAL NETWORKS
- 4. MATERIALS AND METHODS
- 6. CONCLUSION
- REFERENCES
- Chapter 9: Predictive Modeling for Classification of Breast Cancer Dataset Using Feature Selection Techniques
- ABSTRACT
- INTRODUCTION
- FEATURE SELECTION METHODS
- LITERATURE REVIEW
- PROPOSED METHODS
- METHODS
- RESULT AND DISCUSSION
- CONCLUSION
- REFERENCES
- Chapter 10: Healthcare
- ABSTRACT
- 1. INTRODUCTION
- 2. LITERATURE REVIEW
- 3. PROPOSED METHOD
- 4. RESULT AND DISCUSSION
- 5. CONCLUSION
- REFERENCES
- Chapter 11: Grey Wolf Optimization Trained Feed Foreword Neural Network for Breast Cancer Classification
- ABSTRACT
- INTRODUCTION
- FEED-FORWARD NEURAL NETWORK
- OVERVIEW OF GREY WOLF OPTIMIZATION ALGORITHM (Mirjalili, 2015)
- RESULTS AND DISCUSSION
- CONCLUSION
- ACKNOWLEDGMENT
- REFERENCES
- Chapter 12: Digital Recognition of Breast Cancer Using TakhisisNet
- ABSTRACT
- INTRODUCTION
- BACKGROUND
- RESEARCH METHODOLOGY: TAKHISISNET ARCHITECTURE
- SOLUTIONS AND RECOMMENDATIONS
- FUTURE RESEARCH DIRECTIONS
- CONCLUSION
- ACKNOWLEDGMENT
- REFERENCES
- ADDITIONAL READING
- KEY TERMS AND DEFINITIONS
- ENDNOTES
- APPENDIX
- Chapter 13: Proximate Breast Cancer Factors Using Data Mining Classification Techniques
- ABSTRACT
- INTRODUCTION
- LITERATURE REVIEW
- METHODOLOGY
- INCLUSION AND EXCLUSION CRITERIA
- STATISTICAL DATA MINING TECHNIQUES
- RANDOM FOREST TREE
- NAIVE BAYES MODEL
- CLASSIFICATION AND REGRESSION TREE (CART)
- BOOSTED TREE
- TRAINING AND TESTING OF THE DATASET
- MODEL EVALUATION
- RESULTS AND DISCUSSION
- RESULTS OF THE LEARNING MODELS TO THE BREAST CANCER DATA
- VARIABLE IMPORTANCE
- CONCLUSION
- LIMITATIONS OF STUDY
- FUNDING
- COMPLIANCE WITH ETHICAL STANDARDS
- ACKNOWLEDGMENT
- REFERENCES
- Chapter 14: Similarity Measure of Breast Cancer Datasets Using Fuzzy Rule-Based Classification by Attribute
- ABSTRACT
- INTRODUCTION
- LITERATURE SURVEY
- PROPOSED METHODOLOGY
- EXPERIMENT
- COMPARISON WITH OTHER METHODS
- CONCLUSION
- ACKNOWLEDGMENT
- REFERENCES
- Section 2: Detection Strategies
- Chapter 15: An Artificial Intelligence Approach for the Detection of Cervical Abnormalities
- ABSTRACT
- INTRODUCTION
- MATERIALS AND METHODS
- RESULTS
- DISCUSSION
- CONCLUSION
- REFERENCES
- Chapter 16: The Efforts of Deep Learning Approaches for Breast Cancer Detection Based on X-Ray Images
- ABSTRACT
- INTRODUCTION
- RELATED WORK
- MATERIALS AND METHODS
- EXPERIMENTAL WORKS AND RESULTS
- CONCLUSION
- REFERENCES
- Chapter 17: Analysis of Machine Learning Algorithms for Breast Cancer Detection
- ABSTRACT
- INTRODUCTION
- BACKGROUND AND MOTIVATION
- STRUCTURE AND DEVELOPMENT OF THE BREAST
- TYPES OF BREAST CANCER
- SCREENING METHODS
- OVERVIEW OF BIOPSY
- HISTOPATHOLOGY IMAGE ANALYSIS
- FUTURE RESEARCH DIRECTIONS
- CONCLUSION
- REFERENCES
- KEY TERMS AND DEFINITIONS
- Chapter 18: Machine Learning-Aided Automatic Detection of Breast Cancer
- ABSTRACT
- INTRODUCTION
- LITERATURE REVIEW
- CONCLUSION
- REFERENCES
- Chapter 19: Performance Analysis of Machine Learning Algorithms for Cervical Cancer Detection
- ABSTRACT
- 1. INTRODUCTION
- 2. PROPOSED METHODOLOGY
- 3. RESULT AND FINDINGS
- 4. CONCLUSION AND SCOPE OF FUTURE WORK
- ACKNOWLEDGMENT
- REFERENCES
- Chapter 20: Survey of Breast Cancer Detection Using Machine Learning Techniques in Big Data
- ABSTRACT
- 1. INTRODUCTION
- 2. Breast Cancer
- MACHINE LEARNING
- CHALLENGES
- CONCLUSION
- REFERENCES
- Chapter 21: Machine Learning Techniques for Healthcare Applications
- ABSTRACT
- INTRODUCTION
- OVERVIEW OF ASD
- DATASOURCE
- IMPLEMENTATION AND RESULTS
- CONCLUSION
- CASE STUDY: BREAST CANCER PREDICTION USING SMO AND IBK
- OVERVIEW OF BREAST CANCER
- RELATED WORKS
- CLASSIFICATION TECHNIQUES
- BREAST-CANCER-WISCONSIN DATA SET SUMMARY
- SUMMARY
- REFERENCES
- Chapter 22: Breast Cancer Detection Using Hybrid Computational Intelligence Techniques
- ABSTRACT
- 1. INTRODUCTION
- 2. FOUNDATION OF ROUGH SET
- 3. INTUITIONISTIC FUZZY SET
- 4. POSSIBILISTIC FUZZY C-MEAN SEGMENTATION
- 5. INTUITIONISTIC FUZZY ROUGH HYBRIDIZATION
- 6. INTUITIONISTIC FUZZY HISTOGRAM HYPERBOLIZATION AND POSSIBILISTIC FUZZY C-MEAN HYBRIDIZATION
- 7. RESULT ANALYSIS OF IFRH MODEL
- 8. RESULT ANALYSIS OF IFHHPFCM MODEL
- 9. CONCLUSION
- REFERENCES
- Chapter 23: Computational Studies in Breast Cancer
- ABSTRACT
- INTRODUCTION
- BACKGROUND
- DIFFERENT MOLECULAR FORMS OF BREAST CANCER
- ISSUES WITH MICROARRAY EXPRESSION DATA
- MICROARRAY DATA PREPROCESSING
- EXISTING APPROACHES DEALING WITH MICROARRAY DATA
- BOOSTING
- NEURAL NETWORK
- REGRESSION BASED ALGORITHM
- SVM
- DECISION TREE
- EXPERIMENTAL RESULTS
- PROS AND CONS
- EVALUATION OF CLASSIFIER PERFORMANCE
- CONCLUSION
- REFERENCES
- Chapter 24: Computer Techniques for Detection of Breast Cancer and Follow Up Neoadjuvant Treatment
- ABSTRACT
- INTRODUCTION
- MAIN FOCUS OF THE CHAPTER
- EVALUATING NEOADJUVANT TREATMENT THROUGH INFRARED EXAMINATIONS
- ACQUISITION PROCESSES
- DATABASE FOR MASTOLOGY RESEARCH: STORING AND DISTRIBUTING BREAST EXAMS
- PREPARING BREAST THERMAL EXAMINATIONS FOR ANALYSIS
- REPRESENTING BY DESCRIPTORS THE PROPERTIES OF EXAMS
- CHOOSING THE PROPERTIES THAT BEST REPRESENT EACH EXAM
- EXPERIMENT FOR CHOOSING THE BEST DITE FEATURES FROM DIFFERENT DATASETS
- EXPERIMENTS FOR FOLLOW-UP
- CONCLUSION
- REFERENCES
- Chapter 25: Multi-Agent System Based on Data Mining Algorithms to Detect Breast Cancer
- ABSTRACT
- INTRODUCTION
- MULTI-AGENT SYSTEM
- DATA MINING TECHNIQUES
- DISTRIBUTED DATA MINING AND MULTI-AGENT SYSTEM
- MADM: MULTI-AGENT BASED ON DATA MINING SYSTEM CONCEPTION
- MADM SYSTEM IMPLEMENTATION
- RESULTS AND DISCUSSIONS
- FUTURE RESEARCH DIRECTIONS
- CONCLUSION
- REFERENCES
- Chapter 26: A Novel Fuzzy Frequent Itemsets Mining Approach for the Detection of Breast Cancer
- ABSTRACT
- INTRODUCTION
- RELATED WORK
- WISCONSIN BREAST CANCER DATABASE BACKGROUND
- PRELIMINARIES
- FUZZY ASSOCIATION RULES
- THE PROPOSED IFFP ALGORITHM
- APPLICATIONS OF IFFP ON WBCD
- EXPERIMENTAL EVALUATION
- CONCLUSION
- REFERENCES
- Chapter 27: Thermal Analysis of Realistic Breast Model With Tumor and Validation by Infrared Images
- ABSTRACT
- INTRODUCTION
- BACKGROUND
- METHODS
- THERMAL MODEL
- THERMOPHYSICAL PROPERTIES AND NUMERICAL SIMULATION
- RESULTS
- FUTURESCOPE AND CONCLUSION
- DECLARATION OF COMPETING INTEREST
- REFERENCES
- KEY TERMS AND DEFINITIONS
- Chapter 28: Layer-Wise Tumor Segmentation of Breast Images Using Convolutional Neural Networks
- ABSTRACT
- INTRODUCTION
- BACKGROUND
- METHODOLOGY
- CONCLUSION
- FUTURE RESEARCH DIRECTIONS
- FUNDING
- ACKNOWLEDGMENT
- REFERENCES
- Chapter 29: A Survey on Early Detection of Women's Breast Cancer Using IoT
- ABSTRACT
- INTRODUCTION
- BREAST CANCER SCREENING TEST
- RELATED WORK
- CONCLUSION
- REFERENCES
- Chapter 30: Breast Cancer Detection Using Random Forest Classifier
- ABSTRACT
- INTRODUCTION
- RELATED WORK
- METHODOLOGY
- CONCLUSION
- REFERENCES
- Chapter 31: Breast Ultrasound Image Processing
- ABSTRACT
- INTRODUCTION
- BACKGROUND
- CONTRAST ENHANCEMENT
- DEFUZZIFICATION
- BUSIMAGES AFTER CONTRAST ENHANCEMENT
- SEGMENTATION
- TEXTURE FEATURES
- ADJACENCY INFORMATION AND LOCATION PROBABILITY
- STATISTICAL CLASSIFICATION
- METRICS FOR EVALUATING THE SEGMENTATION
- FUTURE RESEARCH DIRECTIONS - DEEP LEARNING BASED METHODS FOR LESION DETECTION AND SEGMENTATION
- CONCLUSION
- REFERENCES
- ADDITIONAL READING
- KEY TERMS AND DEFINITIONS
- ENDNOTE
- Chapter 32: A Computer-Assisted Diagnostic (CAD) of Screening Mammography to Detect Breast Cancer Without a Surgical Biopsy
- ABSTRACT
- 1. INTRODUCTION AND PROBLEMATIC
- 2. REVIEW OF LITERATURE
- 3. THE PROPOSED METHOD
- 4. THE DIGITAL DATABASE FOR SCREENING MAMMOGRAPHY (DDSM)
- 5. RESULTS AND DISCUSSION
- 6. CONCLUSION AND FUTURE WORKS
- REFERENCES
- Chapter 33: Breast Cancer Lesion Detection From Cranial-Caudal View of Mammogram Images Using Statistical and Texture Features Extraction
- ABSTRACT
- INTRODUCTION
- BACKGROUND
- METHODOLOGY
- RESULTS
- DISCUSSION
- CONCLUSION
- ACKNOWLEDGMENT
- REFERENCES
- Chapter 34: Entropy-Based Breast Cancer Detection in Digital Mammograms Using World Cup Optimization Algorithm
- ABSTRACT
- 1. INTRODUCTION
- 2. MATERIALS AND METHOD
- 3. EXPERIMENTAL IMPLEMENTATION
- 4. RESULTS AND DISCUSSION
- CONCLUSION
- CONFLICT OF INTEREST
- REFERENCES
- Chapter 35: Effect of GLCM Texture Features on the Medio-Lateral Oblique (MLO) View of Digital Mammograms for Breast Cancer Detection
- ABSTRACT
- INTRODUCTION
- BACKGROUND
- METHODOLOGY
- EXPERIMENTAL RESULTS
- DISCUSSION
- CONCLUSION
- ACKNOWLEDGMENT
- REFERENCES
- Section 3: Diagnostic and Prognostic Tools
- Chapter 36: A Survey on Female Breast Cancer
- ABSTRACT
- INTRODUCTION
- BREAST CANCER DIAGNOSIS IMAGING MODALITIES
- BREAST CANCER LESIONS
- COMPUTER-AIDED DIAGNOSIS
- RELATED SURVEY
- CONCLUSION
- REFERENCES
- Chapter 37: Breast Cancer Diagnosis With Mammography
- ABSTRACT
- INTRODUCTION
- BACKGROUND ON BREAST CANCER AND MAMMOGRAPHY
- COMPUTER AIDED DIAGNOSIS (CAD) SYSTEMS
- CBMR-BASED CAD SYSTEMS
- CBMR SCALABILITY ON LARGE-SCALE DATASETS
- CONCLUSION AND FUTURE RESEARCH DIRECTIONS
- REFERENCES
- KEY TERMS AND DEFINITIONS
- Chapter 38: Segmentation of Pectoral Muscle in Mammogram Images Using Gaussian Mixture Model-Expectation Maximization
- ABSTRACT
- INTRODUCTION
- BACKGROUND
- METHODOLOGY
- EXPERIMENTS AND DISCUSSION
- COMPARATIVE STUDY
- CONCLUSION
- REFERENCES
- Chapter 39: An Intelligent-Based Wavelet Classifier for Accurate Prediction of Breast Cancer
- ABSTRACT
- INTRODUCTION
- RELATED WORKS
- PROPOSED METHODOLOGY
- SUMMARY
- REFERENCES
- Chapter 40: Breast Cancer Diagnosis in Mammograms Using Wavelet Analysis, Haralick Descriptors, and Autoencoder
- ABSTRACT
- INTRODUCTION
- BACKGROUND
- PROPOSED METHOD
- SOLUTIONS AND RECOMMENDATIONS
- CONCLUSION
- ACKNOWLEDGMENT
- REFERENCES
- Chapter 41: Using Machine Learning Algorithms for Breast Cancer Diagnosis
- ABSTRACT
- 1. INTRODUCTION
- 2. LITERATURE REVIEW
- 3. METHODOLOGY
- 4. RESULTS AND DISCUSSION
- 5. CONCLUSION AND FUTURE WORK
- REFERENCES
- Chapter 42: Data Mining and Machine Learning Approaches in Breast Cancer Biomedical Research
- ABSTRACT
- INTRODUCTION
- BACKGROUND
- LITERATURE REVIEW
- EXPERIMENTAL EVALUATION
- RESULTS AND DISCUSSION
- CONCLUSION
- REFERENCES
- KEY TERMS AND DEFINITIONS
- Chapter 43: Features Selection Study for Breast Cancer Diagnosis Using Thermographic Images, Genetic Algorithms, and Particle Swarm Optimization
- ABSTRACT
- INTRODUCTION
- BACKGROUND
- METHOD
- RESULTS AND DISCUSSION
- CONCLUSION
- ACKNOWLEDGMENT
- REFERENCES
- Chapter 44: Mobile-Aided Breast Cancer Diagnosis by Deep Convolutional Neural Networks
- ABSTRACT
- BREAST CANCER DIAGNOSIS BY DIRECT MEDICAL IMAGE RECOGNITION
- CNN ARCHITECTURE
- REFERENCES
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