Application Of Artificial Intelligence In Early Detection Of Lung Cancer
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| Author | : Madhuchanda Kar |
| Publisher | : Elsevier |
| Total Pages | : 256 |
| Release | : 2024-05-17 |
| Genre | : Computers |
| ISBN | : 0323952461 |
Application of Artificial Intelligence in Early Detection of Lung Cancer presents the most up-to-date computer-aided diagnosis techniques used to effectively predict and diagnose lung cancer. The presence of pulmonary nodules on lung parenchyma is often considered an early sign of lung cancer, thus using machine and deep learning technologies to identify them is key to improve patients’ outcome and decrease the lethal rate of such disease. The book discusses topics such as basics of lung cancer imaging, pattern recognition techniques, deep learning, and nodule detection and localization. In addition, the book discusses risk prediction based on radiological analysis and 3D modeling. This is a valuable resource for cancer researchers, oncologists, graduate students, radiologists, and members of biomedical field who are interested in the potential of AI technologies in the diagnosis of lung cancer. Provides an overview of the latest developments of artificial intelligence technologies applied to the detection of pulmonary nodules Discusses the different technologies available and guides readers step-by-step to the most applicable one for the specific lung cancer type Describes the entire study design on prediction of lung cancer to help readers apply it to their research successfully
| Author | : Elena Luu |
| Publisher | : |
| Total Pages | : 0 |
| Release | : 2023 |
| Genre | : |
| ISBN | : |
Background: This systematic review examines how the use of artificial intelligence compares to conventional methods in the early detection and accuracy of diagnosing lung and breast cancer. Methods: A comprehensive systematic review was conducted using Google Scholar, ScienceDirect, MDPI Journals, PubMed, JAMA Network, The Lancet Digital Health, Frontiers, Journal of Patient Safety, Thorax, NPJ Breast Cancer, BMC, and Nature Medicine. The inclusion criteria were artificial intelligence models or components of artificial intelligence detecting or classifying breast or lung cancer and articles published within the last five years. The study excluded articles that did not include either breast or lung cancer. The results were compiled into a table based on the key data gathered, such as accuracy, specificity, sensitivity, or P-value. Results: A total of 15 studies were reviewed, eight of the articles were on breast cancer, and seven of the articles were on lung cancer. Each study showed an improvement in their results of accuracy, specificity, and sensitivity. One article gave a confidence score of 63% and two other articles gave a significant P-value
| Author | : Jie Yang |
| Publisher | : Bentham Science Publishers |
| Total Pages | : 154 |
| Release | : 2021-06-01 |
| Genre | : Medical |
| ISBN | : 1681088428 |
Current and Future Application of Artificial Intelligence in Clinical Medicine presents updates on the application of machine learning and deep learning techniques in medical procedures. . Chapters in the volume have been written by outstanding contributors from cancer and computer science institutes with the goal of providing updated knowledge to the reader. Topics covered in the book include 1) Artificial Intelligence (AI) applications in cancer diagnosis and therapy, 2) Updates in AI applications in the medical industry, 3) the use of AI in studying the COVID-19 pandemic in China, 4) AI applications in clinical oncology (including AI-based mining for pulmonary nodules and the use of AI in understanding specific carcinomas), 5) AI in medical imaging. Each chapter presents information on related sub topics in a reader friendly format. The combination of expert knowledge and multidisciplinary approaches highlighted in the book make it a valuable source of information for physicians and clinical researchers active in the field of cancer diagnosis and treatment (oncologists, oncologic surgeons, radiation oncologists, nuclear medicine physicians, and radiologists) and computer science scholars seeking to understand medical applications of artificial intelligence.
| Author | : Abdulhamit Subasi |
| Publisher | : Elsevier |
| Total Pages | : 550 |
| Release | : 2024-03-22 |
| Genre | : Computers |
| ISBN | : 0443223092 |
??Applications of Artificial Intelligence in Healthcare and Biomedicine provides ?updated knowledge on the applications of artificial intelligence in medical image analysis. The book starts with an introduction to Artificial Intelligence techniques for Healthcare and Biomedicine. In 16 chapters it presents artificial applications in Electrocardiogram (ECG), Electroencephalogram (EEG) and Electromyography (EMG), signal analysis, Computed Tomography (CT), Magnetic Resonance Imaging (MR) and Ultrasound image analysis. It equips researchers with tools for early breast cancer detection from mammograms using artificial intelligence (AI), AI models to detect lung cancer using histopathological images and a deep learning-based approach to get a proper and faster diagnosis of the Optical Coherence Tomography (OCT) images. It also presents present 3D medical image analysis using 3D Convolutional Neural Networks (CNNs). Applications of Artificial Intelligence in Healthcare and Biomedicine closes with a chapter on AI-based approach to forecast diabetes patients' hospital re-admissions. This is a valuable resource for clinicians, researchers and healthcare professionals who are interested in learning more about the applications of Artificial Intelligence and its impact in medical/biomedical image analysis. Provides knowledge on Artificial Intelligence algorithms for clinical data analysis Gives insights into both AI applications in biomedical signal analysis, biomedical image analysis, and applications in healthcare, including drug discovery Equips researchers with tools for early breast cancer detection
| Author | : Ayman S. El-Baz |
| Publisher | : |
| Total Pages | : 0 |
| Release | : 2021 |
| Genre | : Diagnostic imaging |
| ISBN | : 9780750333542 |
This book focuses on major trends and challenges in the detection of lung cancer, presenting work aimed at identifying new techniques and their use in biomedical analysis. This volume covers recent advancements in lung cancer and imaging detection and classification, examining the main applications of computer aided diagnosis relating to lung cancer: lung nodule segmentation, lung nodule classification, and Big Data in lung cancer.
| Author | : Khalid Shaikh |
| Publisher | : Springer Nature |
| Total Pages | : 107 |
| Release | : 2020-12-04 |
| Genre | : Technology & Engineering |
| ISBN | : 3030592081 |
This book provides an introduction to next generation smart screening technology for medical image analysis that combines artificial intelligence (AI) techniques with digital screening to develop innovative methods for detecting breast cancer. The authors begin with a discussion of breast cancer, its characteristics and symptoms, and the importance of early screening.They then provide insight on the role of artificial intelligence in global healthcare, screening methods for breast cancer using mammogram, ultrasound, and thermogram images, and the potential benefits of using AI-based systems for clinical screening to more accurately detect, diagnose, and treat breast cancer. Discusses various existing screening methods for breast cancer Presents deep information on artificial intelligence-based screening methods Discusses cancer treatment based on geographical differences and cultural characteristics
| Author | : Xiaoli Lan |
| Publisher | : Frontiers Media SA |
| Total Pages | : 119 |
| Release | : 2022-03-02 |
| Genre | : Medical |
| ISBN | : 2889745538 |
| Author | : Bernard Nordlinger |
| Publisher | : Springer Nature |
| Total Pages | : 275 |
| Release | : 2020-03-17 |
| Genre | : Technology & Engineering |
| ISBN | : 3030321614 |
This book provides an overview of the role of AI in medicine and, more generally, of issues at the intersection of mathematics, informatics, and medicine. It is intended for AI experts, offering them a valuable retrospective and a global vision for the future, as well as for non-experts who are curious about this timely and important subject. Its goal is to provide clear, objective, and reasonable information on the issues covered, avoiding any fantasies that the topic “AI” might evoke. In addition, the book seeks to provide a broad kaleidoscopic perspective, rather than deep technical details.
| Author | : Erik R. Ranschaert |
| Publisher | : Springer |
| Total Pages | : 373 |
| Release | : 2019-01-29 |
| Genre | : Medical |
| ISBN | : 3319948784 |
This book provides a thorough overview of the ongoing evolution in the application of artificial intelligence (AI) within healthcare and radiology, enabling readers to gain a deeper insight into the technological background of AI and the impacts of new and emerging technologies on medical imaging. After an introduction on game changers in radiology, such as deep learning technology, the technological evolution of AI in computing science and medical image computing is described, with explanation of basic principles and the types and subtypes of AI. Subsequent sections address the use of imaging biomarkers, the development and validation of AI applications, and various aspects and issues relating to the growing role of big data in radiology. Diverse real-life clinical applications of AI are then outlined for different body parts, demonstrating their ability to add value to daily radiology practices. The concluding section focuses on the impact of AI on radiology and the implications for radiologists, for example with respect to training. Written by radiologists and IT professionals, the book will be of high value for radiologists, medical/clinical physicists, IT specialists, and imaging informatics professionals.
| Author | : Jessica Vo |
| Publisher | : |
| Total Pages | : 69 |
| Release | : 2020 |
| Genre | : Bioinformatics |
| ISBN | : |
Abstract: In 2018, the top three most common cancer types in the United States were breast cancer, lung cancer, and prostate cancer, in descending order. In 2019, approximately 13% of new cancer types are derived from lung cancer. Most late diagnosed cases are caused by hidden genetic variants and other subjective factors, such as smoking. In this study, we focus on applying supervised machine learning techniques (logistic regression, random forest, gradient boosting, extreme gradient boosting, support vector machine, and Bayesian additive regression trees) to the microarray gene expression data in order to detect those inherited factors which are most correlated to lung cancer development in the Caucasian smoking population. The model validation metrics are the area under the receiver operating characteristic curve, accuracy, sensitivity, specificity, recall, and precision percentages. The most effective model was found to be gradient boosting, which gives the highest prediction power (97.9%), with a recall of 90.9% and a precision of 90.9%.
