Descripción
Alzheimer’s disease is one of the most challenging neurodegenerative disorders affecting millions of people worldwide. Early and accurate diagnosis plays a critical role in improving treatment planning and patient care. This book explores the application of Machine Learning and Deep Learning algorithms for the early-stage detection of Alzheimer’s disease using medical imaging and healthcare data.
The book presents research-oriented methodologies, predictive models, data preprocessing techniques, feature extraction methods, and performance evaluation metrics used in intelligent disease detection systems. It also highlights the role of Convolutional Neural Networks (CNNs), classification algorithms, and AI-driven healthcare analytics in enhancing diagnostic accuracy.
Designed for students, researchers, and healthcare technology enthusiasts, this book provides valuable insights into the intersection of Artificial Intelligence and medical science while demonstrating how modern computational techniques can support early diagnosis and improve healthcare outcomes.
This book provides a comprehensive overview of how machine learning and deep learning techniques can be applied to the early detection of Alzheimer's disease. The concepts are presented in a clear and organized manner, making it useful for both beginners and readers with a technical background. I particularly appreciated the way the book connects AI methodologies with real-world healthcare challenges. It offers valuable insights into predictive modeling, data analysis, and the growing role of artificial intelligence in medical diagnosis. Overall, it is an informative and relevant read for students, researchers, and professionals interested in healthcare technology and AI-driven solutions.
Great work Mr. Vishank Agrohi...
