Saba T. Prognostic Models in Healthcare. AI and Statistical Approaches 2022

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Saba T. Prognostic Models in Healthcare. AI and Statistical Approaches 2022

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Added: 2025-03-10 23:38:59

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Textbook in PDF format This book focuses on contemporary technologies and research in computational intelligence that has reached the practical level and is now accessible in preclinical and clinical settings. This book's principal objective is to thoroughly understand significant technological breakthroughs and research results in predictive modeling in healthcare imaging and data analysis. Machine learning and deep learning could be used to fully automate the diagnosis and prognosis of patients in medical fields. The healthcare industry's emphasis has evolved from a clinical-centric to a patient-centric model. However, it is still facing several technical, computational, and ethical challenges. Big data analytics in health care is becoming a revolution in technical as well as societal well-being viewpoints. Moreover, in this age of big data, there is increased access to massive amounts of regularly gathered data from the healthcare industry that has necessitated the development of predictive models and automated solutions for the early identification of critical and chronic illnesses. The book contains high-quality, original work that will assist readers in realizing novel applications and contexts for deep learning architectures and algorithms, making it an indispensable reference guide for academic researchers, professionals, industrial software engineers, and innovative model developers in healthcare industry. Preface Editors and Contributors Segmentation of White Blood Cells in Acute Myeloid Leukemia Microscopic Images: A Review Computer Vision-Based Prognostic Modelling of COVID-19 from Medical Imaging An Accurate Skin Lesion Classification Using Fused Pigmented Deep Feature Extraction Method COVID-19 Prediction, Diagnosis and Prevention Through Computer Vision Health Monitoring Methods in Heart Diseases Based on Data Mining Approach: A Directional Review Machine Learning-Based Brain Diseases Diagnosing in Electroencephalogram Signals, Alzheimer’s, and Parkinson's Skin Lesion Detection Using Recent Machine Learning Approaches Improving Monitoring and Controlling Parameters for Alzheimer's Patients Based on IoMT A Novel Method for Lung Segmentation of Chest with Convolutional Neural Network Leukemia Detection Using Machine and Deep Learning Through Microscopic Images—A Review A Review on Machine Learning-Based WBCs Analysis in Blood Smear Images: Key Challenges, Datasets, and Future Directions Automatic Detection of Liver Cancer Using Artificial Intelligence and Imaging Techniques—A Review Spot Filtering Adaptive Thresholding (SFAT) Method for Early Pigment Spot Detection on Iris Surface A Big Survey on Biometrics for Human Identification Computer-Aided Diagnosis of Pneumothorax Through X-Ray Images Using Deep Learning—A Review ML and DL Architectures Comparisons for the Classification of COVID-19 Using Chest X-Ray Images Data Mining in Medical Laboratory Service Improves Disease Surveillance and Quality Healthcare Deep Learning-Based Lung Infection Detection Using Radiology Modalities and Comparisons on Benchmark Datasets in COVID-19 Pandemic