Machine learning models for predicting anomaly in scanned images
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Finney Daniel Shadrach
Abstract
The use of multimodal datamultimodal data has been revealed to be a paradigm shift within and across different fields for anomalous behavior diagnosis, with improved performance and standard measures of returns on investment. Precisely, this book chapter offers a systematic literature review of studies on multimodal anomaly detection, as well as the techniques used in the different phases of the MDL process acquisition, preprocessing, feature extraction, modeling, and evaluation based on the proposed taxonomy, consequently, highlighting the applications and challenges in industrial, medical, and real-time settings. It also covers state-of-the-art approaches, including feature-level fusion, decision-level fusion, and model-level fusion, the use of deep learning, and graph-based methods. The chapter focuses on the examples of using these techniques in practice, showing how automotive manufacturing, aerospace maintenance, semiconductor production, and medical imaging industries can benefit from them. Some of the main issues like the scalability in big data, data integration, and real-time processing are solved with modern solutions, and future perspectives are shown. Thus, this chapter is most relevant for researchers and practitioners who look for means to enhance accuracy and ROI of the anomaly detection via multimodal analysismultimodal analysis.
Abstract
The use of multimodal datamultimodal data has been revealed to be a paradigm shift within and across different fields for anomalous behavior diagnosis, with improved performance and standard measures of returns on investment. Precisely, this book chapter offers a systematic literature review of studies on multimodal anomaly detection, as well as the techniques used in the different phases of the MDL process acquisition, preprocessing, feature extraction, modeling, and evaluation based on the proposed taxonomy, consequently, highlighting the applications and challenges in industrial, medical, and real-time settings. It also covers state-of-the-art approaches, including feature-level fusion, decision-level fusion, and model-level fusion, the use of deep learning, and graph-based methods. The chapter focuses on the examples of using these techniques in practice, showing how automotive manufacturing, aerospace maintenance, semiconductor production, and medical imaging industries can benefit from them. Some of the main issues like the scalability in big data, data integration, and real-time processing are solved with modern solutions, and future perspectives are shown. Thus, this chapter is most relevant for researchers and practitioners who look for means to enhance accuracy and ROI of the anomaly detection via multimodal analysismultimodal analysis.
Chapters in this book
- Frontmatter I
- Contents V
- List of contributors VII
- Deep learning in computer vision 1
- Deep learning for medical image segmentation 51
- Deep learning for image segmentation 107
- Machine learning algorithm for medical image processing 155
- Machine learning models for predicting anomaly in scanned images 215
- Advanced machine learning models for accurate and efficient anomaly detection in scanned visual data 263
- AI-enhanced diagnostic materials improving sensitivity for disease detection and diagnostics 311
- Machine learning approaches for optimizing the synthesis and functionalization of quantum dots for medical imaging 353
- Machine learning application in tissue engineering: scaffold design 407
- Machine learning approaches to improve electrospun nanofibers’ performance and properties for medical applications 441
- Predictive machine learning models for assessing the long-term stability of biodegradable scaffolds 483
- Customization of medical implants using 3D printing 523
- Index 559
- De Gruyter Series in Advanced Mechanical Engineering
Chapters in this book
- Frontmatter I
- Contents V
- List of contributors VII
- Deep learning in computer vision 1
- Deep learning for medical image segmentation 51
- Deep learning for image segmentation 107
- Machine learning algorithm for medical image processing 155
- Machine learning models for predicting anomaly in scanned images 215
- Advanced machine learning models for accurate and efficient anomaly detection in scanned visual data 263
- AI-enhanced diagnostic materials improving sensitivity for disease detection and diagnostics 311
- Machine learning approaches for optimizing the synthesis and functionalization of quantum dots for medical imaging 353
- Machine learning application in tissue engineering: scaffold design 407
- Machine learning approaches to improve electrospun nanofibers’ performance and properties for medical applications 441
- Predictive machine learning models for assessing the long-term stability of biodegradable scaffolds 483
- Customization of medical implants using 3D printing 523
- Index 559
- De Gruyter Series in Advanced Mechanical Engineering
