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Computational intelligence and modelling techniques for disease detection in mammogram images /

Computational Intelligence and Modelling Techniques for Disease Detection in Mammogram Images comprehensively examines the wide range of AI-based mammogram analysis methods for medical applications. Beginning with an introductory overview of mammogram data analysis, the book covers the current techn...

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Bibliographic Details
Corporate Author: ScienceDirect (Online service)
Other Authors: Hemanth, D. Jude
Format: eBook
Language:English
Published: London : Academic Press, 2024.
Subjects:
Breast > Cancer > Diagnosis.
Breast > Cancer > Imaging.
Artificial intelligence > Medical applications.
Sein > Cancer > Imagerie.
Intelligence artificielle > Applications en médecine.
Electronic books.
Online Access:Connect to the full text of this electronic book
Table of Contents:
  • Front Cover
  • Computational Intelligence and Modelling Techniques for Disease Detection in Mammogram Images
  • Computational Intelligence and Modelling Techniques for Disease Detection in Mammogram Images
  • Copyright
  • Contents
  • Contributors
  • Preface
  • 1
  • Mammogram data analysis: Trends, challenges, and future directions
  • 1. Introduction
  • 1.1 Theoretical background
  • 1.1.1 "Sick lobe" model
  • 1.1.2 Neoductgenesis
  • 1.2 Technical knowledge
  • 1.3 BC diagnosis using several imaging modalities
  • 1.3.1 Mammography
  • 1.3.2 Ultrasound
  • 1.3.3 MRI
  • 1.3.4 Histopathology
  • 1.3.5 Thermography
  • 1.4 Risk factors
  • 1.5 Advantages in mammography
  • 2. Related works
  • 2.1 Microcalcification detection
  • 2.2 Classification of mass
  • 2.3 Feature-based BC detection
  • 2.4 Computer-aided mammography
  • 2.5 Database for mammogram images
  • 2.5.1 INbreast
  • 2.5.2 CBIS-DDSM
  • 2.5.3 Image retrieval in medical applications
  • 2.5.4 Mammographic Image Analysis Society
  • 2.5.5 Breast cancer digital repository
  • 2.5.6 BancoWeb LAPIMO
  • 2.5.7 UCHC DigiMammo
  • 3. Current trends in mammography analysis
  • 3.1 Full field digital mammography
  • 3.2 Digital mammography
  • 3.2.1 Computed tomography electro-optical tomographic laser mammography
  • 3.3 Scintimammography
  • 3.4 Optical mammography
  • 3.5 Digital breast tomosynthesis
  • 3.6 Future of DBT imaging
  • 4. Challenges in mammogram data analysis
  • 4.1 General challenges in BC measurement and analysis
  • 4.1.1 Shortcomings in primary care
  • 4.1.2 Public secondary healthcare clinic mammography concerns
  • 4.1.3 A gap between the BC detection strategy for primary care and secondary
  • 4.1.4 Potential risks of mammography
  • 4.1.5 Physical and mental suffering
  • 4.1.6 Biopsies
  • 4.2 Obstacles to data analytics in BC
  • 4.2.1 Personal encounters and obstacles to obtaining assistance
  • 4.2.2 Connecting theory into practice
  • 4.2.3 Carrying out mammograms
  • 4.2.4 Communication
  • 4.3 Breast density versus mammographic sensitivity
  • 4.4 False alarms
  • 4.5 Radiation dose and digital breast tomosynthesis
  • 4.6 Artifacts caused by surgical staples
  • 4.6.1 Imbalanced database
  • 4.6.2 Insufficient standardization
  • 4.7 Challenges in data analytics models
  • 4.8 Robustness
  • 4.9 Cyber security
  • 5. Future directions of mammogram analysis
  • 6. Conclusion
  • References
  • 2
  • AI in breast imaging: Applications, challenges, and future research
  • 1. Introduction
  • 1.1 Breast cancer: Statistics
  • 1.2 Breast imaging techniques and common breast abnormalities
  • 1.3 Mammogram datasets
  • 2. Toward AI for breast cancer diagnosis
  • 2.1 AI applications for mammogram-based breast cancer analysis
  • 2.1.1 Breast abnormality identification and categorization
  • 2.1.2 Breast mass segmentation
  • 2.1.3 Breast density assessment
  • 2.1.4 Breast cancer risk assessment
  • 2.1.5 BI-RADS classification
  • 2.1.6 Axillary node assessment
  • 2.2 Challenges and future research