Chapter 8 AI applications in brain cancer therapy
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Abstract
The timely identification of cancer is crucial in order to potentially save several lives. Tumours in the brain can be categorised based on their type, location, rate of growth, and stage of advancement. Therefore, the classification of tumours is essential for targeted treatment. Brain tumour segmentation is the process of precisely identifying and outlining the specific regions of brain tumours. An expert with comprehensive knowledge of neurological disorders is required to manually determine the specific classification of a brain tumour. Moreover, the task of processing several photos is time-consuming and exhausting. Hence, the utilisation of automated segmentation and classification methods is necessary to expedite and improve the diagnosis of brain tumours. Brain tumours can be rapidly and securely identified through the use of imaging techniques such as computed tomography, magnetic resonance imaging, and other modalities. Machine learning and artificial intelligence (AI) have demonstrated potential in creating algorithms that assist in the automatic categorisation and division of data using different imaging techniques. This chapter explores the latest breakthroughs in the utilisation of AI in the field of brain cancer, which is a major worldwide health concern. AI has revolutionised the field of brain tumour management by employing advanced imaging, histological, and genomic methods. These technologies enable effective diagnosis, categorisation, outcome prediction, and treatment planning.
Abstract
The timely identification of cancer is crucial in order to potentially save several lives. Tumours in the brain can be categorised based on their type, location, rate of growth, and stage of advancement. Therefore, the classification of tumours is essential for targeted treatment. Brain tumour segmentation is the process of precisely identifying and outlining the specific regions of brain tumours. An expert with comprehensive knowledge of neurological disorders is required to manually determine the specific classification of a brain tumour. Moreover, the task of processing several photos is time-consuming and exhausting. Hence, the utilisation of automated segmentation and classification methods is necessary to expedite and improve the diagnosis of brain tumours. Brain tumours can be rapidly and securely identified through the use of imaging techniques such as computed tomography, magnetic resonance imaging, and other modalities. Machine learning and artificial intelligence (AI) have demonstrated potential in creating algorithms that assist in the automatic categorisation and division of data using different imaging techniques. This chapter explores the latest breakthroughs in the utilisation of AI in the field of brain cancer, which is a major worldwide health concern. AI has revolutionised the field of brain tumour management by employing advanced imaging, histological, and genomic methods. These technologies enable effective diagnosis, categorisation, outcome prediction, and treatment planning.
Chapters in this book
- Frontmatter I
- Preface V
- Foreword VII
- Contents IX
- About the authors XVII
- Chapter 1 Artificial intelligence in cancer treatment and management 1
- Chapter 2 AI-based approaches to cancer drug discovery 27
- Chapter 3 Integrating AI and digital twin technology in cancer therapy 47
- Chapter 4 AI for enhanced cancer detection and diagnosis 63
- Chapter 5 AI-guided surgical interventions for cancer and tumor removal 99
- Chapter 6 Artificial intelligence in breast cancer management 121
- Chapter 7 AI innovations in colorectal cancer detection and treatment 143
- Chapter 8 AI applications in brain cancer therapy 165
- Chapter 9 Leveraging AI for liver cancer diagnosis and treatment 191
- Chapter 10 AI-driven advances in lung cancer care 213
- Chapter 11 Artificial intelligence in prostate cancer detection and management 243
- Chapter 12 AI solutions for skin cancer diagnosis and treatment 265
- Index 345
- De Gruyter Series on the Applications of Mathematics in Engineering and Information Sciences
Chapters in this book
- Frontmatter I
- Preface V
- Foreword VII
- Contents IX
- About the authors XVII
- Chapter 1 Artificial intelligence in cancer treatment and management 1
- Chapter 2 AI-based approaches to cancer drug discovery 27
- Chapter 3 Integrating AI and digital twin technology in cancer therapy 47
- Chapter 4 AI for enhanced cancer detection and diagnosis 63
- Chapter 5 AI-guided surgical interventions for cancer and tumor removal 99
- Chapter 6 Artificial intelligence in breast cancer management 121
- Chapter 7 AI innovations in colorectal cancer detection and treatment 143
- Chapter 8 AI applications in brain cancer therapy 165
- Chapter 9 Leveraging AI for liver cancer diagnosis and treatment 191
- Chapter 10 AI-driven advances in lung cancer care 213
- Chapter 11 Artificial intelligence in prostate cancer detection and management 243
- Chapter 12 AI solutions for skin cancer diagnosis and treatment 265
- Index 345
- De Gruyter Series on the Applications of Mathematics in Engineering and Information Sciences
