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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| Formato: | Electrónico eBook |
| Lenguaje: | inglés |
| Publicado: |
London :
Academic Press,
2024.
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| Materias: | |
| Acceso en línea: | ScienceDirect |
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| 050 | 4 | |a RC280.B8 | |
| 082 | 0 | 4 | |a 616.99449075 |1 https://id.oclc.org/worldcat/ddc/E4bgqqCd3qFdVYFFxv8mPm3Kjf |
| 245 | 0 | 0 | |a Computational intelligence and modelling techniques for disease detection in mammogram images / |c edited by D. Jude Hemanth. |
| 264 | 1 | |a London : |b Academic Press, |c 2024. | |
| 300 | |a 1 online resource | ||
| 336 | |a text |b txt |2 rdacontent | ||
| 337 | |a computer |b c |2 rdamedia | ||
| 338 | |a online resource |b cr |2 rdacarrier | ||
| 505 | 0 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 505 | 8 | |a 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 | |
| 500 | |a Includes index. | ||
| 588 | 0 | |a Print version record. | |
| 520 | |a 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 technologies such as ultrasound, molecular breast imaging (MBI), magnetic resonance (MR), and Positron Emission mammography (PEM), as well as the recent advancements in 3D breast tomosynthesis and 4D mammogram. Deep learning models are presented in each chapter to show how they can assist in the efficient processing of breast images. The book also discusses hybrid intelligence approaches for early-stage detection and the use of machine learning classifiers for cancer detection, staging and density assessment in order to develop a proper treatment plan. This book will not only aid computer scientists and medical practitioners in developing a real-time AI based mammogram analysis system, but also addresses the issues and challenges with the current processing methods which are not conducive for real-time applications. Presents novel ideas for AI based mammogram data analysis Discusses the roles deep learning and machine learning techniques play in efficient processing of mammogram images and in the accurate defining of different types of breast cancer Features dozens of real-world case studies from contributors across the globe. | ||
| 650 | 0 | |a Breast |x Cancer |x Diagnosis. | |
| 650 | 0 | |a Breast |x Cancer |x Imaging. | |
| 650 | 0 | |a Artificial intelligence |x Medical applications. | |
| 650 | 6 | |a Sein |0 (CaQQLa)201-0071080 |x Cancer |0 (CaQQLa)201-0071080 |x Imagerie. |0 (CaQQLa)201-0377501 | |
| 650 | 6 | |a Intelligence artificielle |x Applications en médecine. |0 (CaQQLa)201-0180593 | |
| 650 | 7 | |a Artificial intelligence |x Medical applications |2 fast |0 (OCoLC)fst00817267 | |
| 650 | 7 | |a Breast |x Cancer |x Diagnosis |2 fast |0 (OCoLC)fst00838268 | |
| 650 | 7 | |a Breast |x Cancer |x Imaging |2 fast |0 (OCoLC)fst00838282 | |
| 700 | 1 | |a Hemanth, D. Jude. | |
| 776 | 0 | 8 | |i Print version: |t Computational intelligence and modelling techniques for disease detection in mammogram images. |d Amsterdam : Academic Press, 2023 |z 9780443139994 |w (OCoLC)1400077793 |
| 856 | 4 | 0 | |3 ScienceDirect |u https://www.sciencedirect.com/science/book/9780443139994 |