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Classification Methods in Image Analysis with a Special Focus on Medical Analytics

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Machine Learning Paradigms

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 149 ))

Abstract

This paper describes the design and application of classification methods for image analysis and processing. Accordingly, the main trends and challenges of the machine learning are presented in multiple contexts where the image analysis plays a very important role, including security and biometrics, aerospace and satellite monitoring, document analysis, natural language understanding, and information retrieval. This is accomplished by introducing a categorisation of the most challenging classification methods according to the thematic context and classification typology. Hence, supervised and unsupervised classification methods are presented and discussed. It is followed by a special focus on the medical context, where the classification methods for image analysis are of prior importance in supporting the medical diagnosis process. Accordingly, the second part of the paper surveys the recent and current research in medical analytics where the image classification is a key aspect, and tracks the horizon of the research for future challenges in the field.

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Authors and Affiliations

  1. Faculty of Medicine and Surgery, University of Bologna, Via Massarenti 9, 40138, Bologna, Italy

    Lucio Amelio

  2. DIMES University of Calabria, Via Pietro Bucci 44, 87036, Rende, CS, Italy

    Alessia Amelio

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  1. Lucio Amelio
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  2. Alessia Amelio
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Correspondence to Lucio Amelio .

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Editors and Affiliations

  1. University of Piraeus , Piraeus, Greece

    George A. Tsihrintzis

  2. University of Piraeus , Piraeus, Greece

    Dionisios N. Sotiropoulos

  3. Faculty of Engineering and Information Technology, Centre for Artificial Intelligence, University of Technology, Sydney, New South Wales, Australia

    Lakhmi C. Jain

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Amelio, L., Amelio, A. (2019). Classification Methods in Image Analysis with a Special Focus on Medical Analytics. In: Tsihrintzis, G., Sotiropoulos, D., Jain, L. (eds) Machine Learning Paradigms. Intelligent Systems Reference Library, vol 149 . Springer, Cham. https://doi.org/10.1007/978-3-319-94030-4_3

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