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The book covers the state-of-the-art artificial intelligence techniques used to diagnose brain and prostate cancer, focusing on non-invasive approaches. Cancer is the leading cause of death worldwide, regardless of the type of malignancy. As recently as 2020, about ten million people died due to cancer worldwide. The early detection of cancer tremendously increases the patient’s chances of survival.
Unfortunately, most cancer patients are diagnosed in the final stages of the disease.
Recently, artificial intelligence and deep learning have been shown to offer significant potential to solve this issue. This volume of the book will focus on the use of artificial intelligence techniques for the early diagnosis of brain and prostate cancer. Prostate cancer is the most common cancer in men in the United States (after skin cancer). It is the second leading cause of death in men in the United States.
Different types of tumor can exist in the brain. These tumors can be benign or malignant and can significantly affect the nervous system, depending on their location and growth rate. Among the topics discussed in the book are the use of artificial intelligence in prostate cancer treatment in conjunction with imaging guided radiation therapy artificial-intelligence-based diagnosis of brain tumor diseases multisite brain tumor segmentation using unified generative adversarial networks automatic segmentation of brain metastases for radiotherapy the classification of hyperspectral in vivo brain tissue bone cancer metastasis classi fication in prostate patients prostate cancer detection using histopathology image analysis and machine learning of gliomas in 3D dynamic contrast-enhanced MRI.
Preface
Artificial Intelligence in prostate cancer treatment with image-guided radiation therapy
Artificial-intelligence-based diagnosis of brain tumor diseases
Multisite brain tumor segmentation using a unified generative adversarial network
Role of artificial intelligence in automatic segmentation of brain metastases for radiotherapy
Applications of artificial intelligence in the fields of brain and prostate cancer
AI-based non-deep learning and deep learning techniques used to accurately predict prostate cancer
Intelligent brain tumor classification using deep convolutional neural networks with transfer learning
Big data applications in radiation oncology: challenges and opportunities
A hybrid approach to the hyperspectral classification of in vivo brain tissue: linear unmixing with spatial coherence and machine learning
Application and post-hoc explainability of deep convolutional neural networks for bone cancer metastasis classification in prostate patients
Prostate cancer detection using histopathology image analysis
Machine learning of gliomas in 3D dynamic contrast enhanced MRI: automatic segmentation and classification