The constantly evolving healthcare industry has experienced tremendous technological advancements that have significantly revolutionized medical imaging. However, with the increasing volume and complexity of medical image data, existing analysis methods must also be updated to be efficient and accurate. This is where the challenge lies-a need for a comprehensive solution that bridges the gap between cutting-edge technology and effective healthcare delivery. Computer-Assisted Analysis for Digital Medicinal Imagery offers a roadmap for navigating the intricate landscape of digital medicinal imagery analysis. Unlocking the power of machine learning and breaking down the basics provides researchers, clinicians, and students with the tools necessary to harness technology and improve healthcare outcomes. This book takes a holistic approach to tackling the challenges of digital medicinal imagery analysis, covering practical topics such as imaging modalities, machine learning applications, and emerging technologies. By equipping readers with the knowledge and tools needed to overcome these obstacles, it lays the foundation for a future where healthcare delivery is not just efficient, but transformative.