Название: Artificial Intelligence in e-Health Framework, Volume 1: AI, Classification, Wearable Devices, and Computer-Aided DiagnosisАвтор: Sudip Paul, Jasjit S. Suri
Издательство: Academic Press/Elsevier
Год: 2025
Страниц: 344
Язык: английский
Формат: epub (true)
Размер: 19.8 MB
Artificial Intelligence in e-health Framework, Volume One: AI, Classification, Wearable Devices, and Computer-Aided Diagnosis presents a variety of AI techniques and applications for solving issues in the healthcare industry. As Artificial Intelligence is increasingly incorporated into medical systems and methods, it is critical to understand the formulations and basics of Machine Learning and Deep Learning as well as how to implement these advances into practice. This book specifically explores Artificial Intelligence developments in disease diagnosis, health monitoring, medical image recognition, and diagnostics, as well as e-health records management.
Deep Learning, an AI approach, trains computers to emulate human brain processes. Deep Learning models yield precise predictions by discerning intricate images, text, and sound patterns. These methods automate human-like tasks like image description or sound-to-text transcription. Artificial Neural Networks (ANNs) replicate the human brain’s structure and function, comprising interconnected nodes, or “neurons,” organized into layers: input, hidden, and output. ANNs process input data through these layers to make predictions. They offer advantages over traditional statistical models and conventional regression, including adaptability, speed, nonlinearity, self-learning, and fault tolerance, making them ideal for tackling complex challenges across various domains. The ANN architecture involves three layers: input, hidden, and output, encompassing parameter selection, training, and testing. Crucial parameters include activation functions, neuron count, hidden layers, and training algorithms. ANNs are supervised learning models trained on input and output data, with learning algorithms fine-tuning network parameters such as weights and biases. During training, the objective is to minimize error signals through backpropagation. Subsequent testing and validation phases assess ANN effectiveness using diverse metrics. ANNs find applications in fields like system diagnosis, engineering, and science.
This is a valuable resource for health professionals, scientists, researchers, students, and all who wish to broaden their knowledge in this advancing technology.
Key Features:
Provides an in-depth introduction to Artificial Intelligence in e-health framework
Reviews theoretical and application information to develop understanding of AI advances in diagnostics, health monitoring, and records management
Discusses advanced AI techniques in both Machine Learning and Deep Learning for solving healthcare industry issues
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