Artificial Neural Networks for Image Analysis : A Deep Learning Perspective

Artificial Neural Networks for Image Analysis: A Deep Learning Perspective** presents a clear, practice-oriented guide to applying modern neural networks for visual understanding. The book introduces core concepts—perceptrons, backpropagation, activation functions, optimization, and regularization then advances to convolutional neural networks, transfer learning, and attention-based models used in today’s image pipelines. Readers learn how to design datasets, perform preprocessing and augmentation, select loss functions and metrics, and handle class imbalance and overfitting. Comprehensive chapters cover image classification, object detection, semantic segmentation, and feature extraction, with real-world case studies from medical imaging, remote sensing, security, and industrial inspection. Each unit includes intuitive explanations, workflow diagrams, and hands-on exercises to strengthen implementation skills. Emphasizing both theory and deployment readiness, the book also discusses interpretability, uncertainty, and ethical considerations, making it ideal for students, researchers, and professionals seeking a deep learning–driven perspective on image analysis.