Affiliation:
1. Department of Interdisciplinary Courses in Engineering, Chitkara University Institute of Engineering and Technology,Chitkara University, India
2. Department of Interdisciplinary Courses in Engineering, Chitkara University Institute of Engineering and Technology, Chitakara University, India
3. Chitkara University Institute of Engineering and Technology, Chitkara University, India
Abstract
An intelligent supply chain is essential in the continuously changing environment of the healthcare industry because it combines modern technology, data analytics, and artificial intelligence. Artificial intelligence-driven radiomics enables the extraction of intricate details from medical images, allowing for the early detection and diagnosis of cancer. These algorithms can identify subtle patterns and features in imaging data that might go unnoticed by human observers. Early detection is critical for improving survival rates and treatment outcomes. In this chapter, a review is done on convolutional neural networks (CNNs), transfer learning, ensemble models, radiomics features and machine learning, deep learning for histopathology, multi-modal integration, risk assessment models, and real-time image analysis. The review compresses work on parameters like cancer type, dataset size, accuracy, complexity, and applications of these AI techniques.
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