swMATH ID: 39952
Software Authors: Folorunso, Sakinat Oluwabukonla; Awotunde, Joseph Bamidele; Ayo, Femi Emmanuel; Abdullah, Khadijah-Khuburah Adebisi
Description: RADIoT: the unifying framework for IoT, radiomics and deep learning modeling. The IoT revolution reshapes contemporary healthcare systems by incorporate economic, social, and technological prospects. It is progressing from conventional healthcare systems to more personalized healthcare systems, where patients can be monitored, diagnosed and treated effortlessly. Radiomics is a sub-field of machine learning (ML) that mines quantitative features from radiological images relying on an image-based approach with ML models, which procure information surpassing orthodox medical imaging analysis as diagnosis, prognosis, prediction and response to therapy. The upsurge in the number of radiological images increases the workload of radiologist which in turns decreases their performance, thus they can only detect and evaluate a small portion of information present in images within a short-time. Hence, there is need for a better method for the increase in radiological image selection, detection and evaluation processes thereby reducing the workload of experts. Therefore, this chapter discusses the different types and sources of radiological data, feature extraction and selection method for image analysis. The chapter also presents different ML models ideal for the radiomics and parameter tuning. The challenges, applicability and limitations of Radiomics are also described in this chapter. The radiomic process involves radiological image gathering, segmentation, feature extraction and selection, model building and evaluation. Each of the stage of the process workflow is carefully evaluated for development of a reliable, effective and robust model to be shifted into medical practice for disease diagnosis and prognosis response to treatment.
Homepage: https://link.springer.com/chapter/10.1007%2F978-981-16-2972-3_6
Keywords: radiomics; Internet of things; deep learning; image segmentation; texture descriptors; quantitative imaging
Cited in: 0 Publications