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A Comprehensive Review on Artificial Intelligence-Driven Radiomics for Early Cancer Detection and Intelligent Medical Supply Chain

A Comprehensive Review on Artificial Intelligence-Driven Radiomics for Early Cancer Detection and Intelligent Medical Supply Chain

Rajneesh Talwar, Manvinder Sharma, Sonia
Copyright: © 2024 |Pages: 29
ISBN13: 9798369313473|ISBN13 Softcover: 9798369344651|EISBN13: 9798369313480
DOI: 10.4018/979-8-3693-1347-3.ch015
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MLA

Talwar, Rajneesh, et al. "A Comprehensive Review on Artificial Intelligence-Driven Radiomics for Early Cancer Detection and Intelligent Medical Supply Chain." AI and Machine Learning Impacts in Intelligent Supply Chain, edited by Binay Kumar Pandey, et al., IGI Global, 2024, pp. 226-254. https://doi.org/10.4018/979-8-3693-1347-3.ch015

APA

Talwar, R., Sharma, M., & Sonia. (2024). A Comprehensive Review on Artificial Intelligence-Driven Radiomics for Early Cancer Detection and Intelligent Medical Supply Chain. In B. Pandey, U. Kanike, A. George, & D. Pandey (Eds.), AI and Machine Learning Impacts in Intelligent Supply Chain (pp. 226-254). IGI Global. https://doi.org/10.4018/979-8-3693-1347-3.ch015

Chicago

Talwar, Rajneesh, Manvinder Sharma, and Sonia. "A Comprehensive Review on Artificial Intelligence-Driven Radiomics for Early Cancer Detection and Intelligent Medical Supply Chain." In AI and Machine Learning Impacts in Intelligent Supply Chain, edited by Binay Kumar Pandey, et al., 226-254. Hershey, PA: IGI Global, 2024. https://doi.org/10.4018/979-8-3693-1347-3.ch015

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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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