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This book explores Deep Learning strategies to overcome the challenges of Class Imbalance, Multi-Class Classification, and Multi-Modality Issues in Alzheimer's Disease and Dementia diagnosis. It introduces optimized neural models with better data enhancement techniques to improve the classification accuracy, supporting early and precise detection using MRI, PET, Genetical and Clinical data.This book serves as a detailed guide for researchers, data scientists, and healthcare experts focusing on AI-based Alzheimer's detection. It offers insights into building advanced, efficient, and scalable…mehr

Produktbeschreibung
This book explores Deep Learning strategies to overcome the challenges of Class Imbalance, Multi-Class Classification, and Multi-Modality Issues in Alzheimer's Disease and Dementia diagnosis. It introduces optimized neural models with better data enhancement techniques to improve the classification accuracy, supporting early and precise detection using MRI, PET, Genetical and Clinical data.This book serves as a detailed guide for researchers, data scientists, and healthcare experts focusing on AI-based Alzheimer's detection. It offers insights into building advanced, efficient, and scalable diagnostic models for neurodegenerative diseases, helping improve accuracy and reliability in early diagnosis.
Autorenporträt
Dr. Neetha P U (BMSIT&M, Bangalore) specializes in Deep Learning for Alzheimer's Diagnosis. She holds a Ph.D. from UVCE, Bengaluru, has published in ACM, Springer, IEEE, and holds two patents. Dr. Pushpa C N working as Asociate Professor at UVCE, Bangalore and has 25 years of teaching experience. She has published 65 papers, holds five patents.