Application of Artificial Intelligence in Early Detection of Lung Cancer

Artificial Intelligence in Early Detection of Lung Cancer provides computer-aided diagnosis techniques to predict and diagnose lung cancer, focusing on pulmonary nodules and risk prediction based on radiological analysis and 3D modeling. It is a valuable resource for cancer researchers, oncologists, graduate students, radiologists, and biomedical field members.

Format: Paperback / softback
Length: 254 pages
Publication date: 01 March 2024
Publisher: Elsevier Science & Technology

Artificial Intelligence (AI) has emerged as a powerful tool in the early detection of lung cancer, offering promising techniques for accurate prediction and diagnosis. The presence of pulmonary nodules on lung parenchyma is often considered an early sign of lung cancer, and using machine and deep learning technologies to identify them is crucial in improving patient outcomes and reducing the lethal rate of this disease. The book "Application of Artificial Intelligence in Early Detection of Lung Cancer" provides a comprehensive overview of the latest computer-aided diagnosis techniques used in lung cancer diagnosis. It covers various topics such as lung cancer imaging, pattern recognition techniques, deep learning, and nodule detection and localization. Additionally, the book discusses risk prediction based on radiological analysis and 3D modeling, making it a valuable resource for cancer researchers, oncologists, graduate students, radiologists, and members of the biomedical field who are interested in the potential of AI technologies in the diagnosis of lung cancer.

One of the key challenges in lung cancer diagnosis is the identification of pulmonary nodules on lung parenchyma. Traditional imaging techniques such as chest X-ray, computed tomography (CT), and magnetic resonance imaging (MRI) have limited sensitivity and specificity in detecting small nodules. However, AI technologies have the ability to analyze large amounts of data and identify subtle patterns that may be missed by human observers. Machine learning algorithms, for example, can be trained to recognize patterns in lung images and classify them as benign or malignant. Deep learning algorithms, on the other hand, can learn to recognize complex patterns and features in lung images, making them more accurate in detecting nodules.

Another important aspect of lung cancer diagnosis is the prediction of risk based on radiological analysis and 3D modeling. Radiological analysis involves the use of imaging techniques to create images of the lungs and analyze their structure and function. 3D modeling involves the creation of a 3D model of the lungs based on imaging data, which can be used to predict the likelihood of cancer recurrence and metastasis. AI technologies can be used to analyze radiological images and generate 3D models, which can then be used to predict risk.

The book "Application of Artificial Intelligence in Early Detection of Lung Cancer" provides a detailed overview of these techniques and their applications in lung cancer diagnosis. It includes case studies and examples of how AI technologies have been used to improve patient outcomes and reduce the lethal rate of lung cancer. The book also discusses the ethical and legal implications of using AI technologies in healthcare, and provides guidance on how to implement AI technologies in clinical practice.

In conclusion, the book "Application of Artificial Intelligence in Early Detection of Lung Cancer" is a valuable resource for cancer researchers, oncologists, graduate students, radiologists, and members of the biomedical field who are interested in the potential of AI technologies in the diagnosis of lung cancer. It provides a comprehensive overview of the latest computer-aided diagnosis techniques used in lung cancer diagnosis, and discusses the challenges and opportunities associated with using AI technologies in healthcare. By implementing AI technologies in clinical practice, we can improve patient outcomes and reduce the lethal rate of lung cancer.

Weight: 450g
Dimension: 235 x 191 (mm)
ISBN-13: 9780323952453