Diagnosis of Neurological Disorders Based on Deep Learning Techniques

"Diagnosis of Neurological Disorders Based on Deep Learning Techniques" delves into the application of deep learning methodologies for diagnosing various neurological conditions. The book provides a comprehensive overview of foundational deep learning algorithms, illustrated through diagrams, data tables, and practical examples. It specifically addresses the diagnosis of neurodegenerative and neurodevelopmental disorders, making it a valuable resource for those in the field.

The text explores various advanced deep learning architectures, including feed-forward neural networks, convolutional neural networks, graph convolutional networks, and recurrent neural networks. Each approach is discussed in the context of its application to neurological diagnosis, allowing readers to understand how these technologies can enhance diagnostic accuracy. Additionally, the book covers essential data preprocessing techniques, such as scaling, correction, trimming, and normalization, which are crucial for preparing data for effective analysis.

Aimed at graduate students and researchers in biomedical imaging and machine learning, the book emphasizes practical application through real-time case studies and examples. It not only equips readers with the theoretical knowledge needed to grasp deep learning concepts but also guides them in building, training, and deploying various deep learning architectures for diagnostic purposes. Overall, this book serves as a significant resource for advancing the understanding and implementation of deep learning in the diagnosis of neurological disorders.