Machine learning algorithms for enhanced medical image analysis and diagnostics
-
P. Prasant
,
J. Dafni Rose
Abstract
Progress made in medical imaging technologies has led to the creation of complex algorithms in machine learning that improve diagnostics. This chapter offers a brief understanding of how machine learning techniques are applied to medical images. The main techniques discussed are convolutional neural networksconvolutional neural networks, support vector machines, and deep learning architectures; examples of their use in image classification, segmentation, and anomaly detection are given. The investigation incorporates a new algorithmic development that solves computational concerns such as time complexity, interclass variance, and high-dimensional data modeling. The overall applicability of these algorithms in clinical settings was also established through examples that focus on the relative improvements in patient status and diagnostic performance. In this chapter, the author provides useful information to practitioners and researchers in the field of medical imaging to reduce the gap in knowledge between theory developmenttheory development and application.
Abstract
Progress made in medical imaging technologies has led to the creation of complex algorithms in machine learning that improve diagnostics. This chapter offers a brief understanding of how machine learning techniques are applied to medical images. The main techniques discussed are convolutional neural networksconvolutional neural networks, support vector machines, and deep learning architectures; examples of their use in image classification, segmentation, and anomaly detection are given. The investigation incorporates a new algorithmic development that solves computational concerns such as time complexity, interclass variance, and high-dimensional data modeling. The overall applicability of these algorithms in clinical settings was also established through examples that focus on the relative improvements in patient status and diagnostic performance. In this chapter, the author provides useful information to practitioners and researchers in the field of medical imaging to reduce the gap in knowledge between theory developmenttheory development and application.
Kapitel in diesem Buch
- Frontmatter I
- Contents V
- List of contributors VII
- Blockchain technology to secure medical data sharing in machine learning applications ensure privacy and integrity 1
- AI-powered sensors and devices for sustained health tracking 39
- Development of AI-driven biomedical sensors and devices optimization for continuous health monitoring 89
- Design and development of AI-driven biomedical sensors and devices and their optimization for continuous health monitoring 131
- Machine learning-driven personalized medicine: customized drug delivery systems and patient-specific material applications 193
- Personalized medicine using customized drug delivery systems and patient-specific material solutions, enabled by machine learning algorithms 239
- AI-driven drug design exploring molecular docking and lead optimization using machine learning algorithms 297
- Machine learning models for predicting drug toxicity and side effects 335
- Machine learning innovations in biomedical materials from drug discovery to personalized medicine 395
- High-throughput screening for novel medical materials: machine learning-enabled approaches 445
- Automated materials characterization using machine learning for screening biocompatible materials 489
- Machine learning algorithms for enhanced medical image analysis and diagnostics 541
- Transforming healthcare with machine learning 585
- Revolutionizing healthcare 635
- Index 687
- De Gruyter Series in Advanced Mechanical Engineering
Kapitel in diesem Buch
- Frontmatter I
- Contents V
- List of contributors VII
- Blockchain technology to secure medical data sharing in machine learning applications ensure privacy and integrity 1
- AI-powered sensors and devices for sustained health tracking 39
- Development of AI-driven biomedical sensors and devices optimization for continuous health monitoring 89
- Design and development of AI-driven biomedical sensors and devices and their optimization for continuous health monitoring 131
- Machine learning-driven personalized medicine: customized drug delivery systems and patient-specific material applications 193
- Personalized medicine using customized drug delivery systems and patient-specific material solutions, enabled by machine learning algorithms 239
- AI-driven drug design exploring molecular docking and lead optimization using machine learning algorithms 297
- Machine learning models for predicting drug toxicity and side effects 335
- Machine learning innovations in biomedical materials from drug discovery to personalized medicine 395
- High-throughput screening for novel medical materials: machine learning-enabled approaches 445
- Automated materials characterization using machine learning for screening biocompatible materials 489
- Machine learning algorithms for enhanced medical image analysis and diagnostics 541
- Transforming healthcare with machine learning 585
- Revolutionizing healthcare 635
- Index 687
- De Gruyter Series in Advanced Mechanical Engineering
