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Hybrid Artificial Intelligence and IoT in Healthcare pp 109–128Cite as

RADIoT: The Unifying Framework for IoT, Radiomics and Deep Learning Modeling

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Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 209))

Abstract

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.

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Authors and Affiliations

  1. Department of Mathematical Science, Olabisi Onabanjo University, Ago-Iwoye, Nigeria

    Sakinat Oluwabukonla Folorunso & Khadijah-Khuburah Adebisi Abdullah

  2. Department of Computer Science, University of Ilorin, Ilorin, Nigeria

    Joseph Bamidele Awotunde

  3. Department of Computer Science, McPherson University, Ogun State, Nigeria

    Femi Emmanuel Ayo

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  1. Sakinat Oluwabukonla Folorunso
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  2. Joseph Bamidele Awotunde
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  3. Femi Emmanuel Ayo
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  4. Khadijah-Khuburah Adebisi Abdullah
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Correspondence to Sakinat Oluwabukonla Folorunso .

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Editors and Affiliations

  1. Department of Computer Science and Engineering, Sikkim Manipal Institute of Technology (SMIT), Sikkim Manipal University, Majitar, Sikkim, India

    Akash Kumar Bhoi

  2. School of Computer Engineering, Kalinga Institute of Industrial Technology Deemed to be University, Bhubaneswar, Odisha, India

    Pradeep Kumar Mallick

  3. Department of Electronics and Communication Engineering, Siksha ‘O’ Anusandhan (Deemed to be University), Bhubaneswar, Odisha, India

    Mihir Narayana Mohanty

  4. Graduate Program on Teleinformatics Engineering, Federal University of Ceará, Fortaleza, Brazil

    Victor Hugo C. de Albuquerque

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Folorunso, S.O., Awotunde, J.B., Ayo, F.E., Abdullah, KK.A. (2021). RADIoT: The Unifying Framework for IoT, Radiomics and Deep Learning Modeling. In: Kumar Bhoi, A., Mallick, P.K., Narayana Mohanty, M., Albuquerque, V.H.C.d. (eds) Hybrid Artificial Intelligence and IoT in Healthcare. Intelligent Systems Reference Library, vol 209. Springer, Singapore. https://doi.org/10.1007/978-981-16-2972-3_6

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