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Medical & Biological Engineering & Computing

, Volume 57, Issue 4, pp 849–862 | Cite as

An extensive study for binary characterisation of adrenal tumours

  • Hasan KoyuncuEmail author
  • Rahime Ceylan
  • Semih Asoglu
  • Hakan Cebeci
  • Mustafa Koplay
Original Article
  • 117 Downloads

Abstract

On adrenal glands, benign tumours generally change the hormone equilibrium, and malign tumours usually tend to spread to the nearby tissues and to the organs of the immune system. These features can give a trace about the type of adrenal tumours; however, they cannot be observed all the time. Different tumour types can be confused in terms of having a similar shape, size and intensity features on scans. To support the evaluation process, biopsy process is applied that includes injury and complication risks. In this study, we handle the binary characterisation of adrenal tumours by using dynamic computed tomography images. Concerning this, the usage of one more imaging modalities and biopsy process is wanted to be excluded. The used dataset consists of 8 subtypes of adrenal tumours, and it seemed as the worst-case scenario in which all handicaps are available against tumour classification. Histogram, grey level co-occurrence matrix and wavelet-based features are investigated to reveal the most effective one on the identification of adrenal tumours. Binary classification is proposed utilising four-promising algorithms that have proven oneself on the task of binary-medical pattern classification. For this purpose, optimised neural networks are examined using six dataset inspired by the aforementioned features, and an efficient framework is offered before the use of a biopsy. Accuracy, sensitivity, specificity, and AUC are used to evaluate the performance of classifiers. Consequently, malign/benign characterisation is performed by proposed framework, with success rates of 80.7%, 75%, 82.22% and 78.61% for the metrics, respectively.

Graphical abstract

Keywords

Adrenal tumours Computed tomography Hybrid classifier Optimisation Tumour classification 

Notes

Acknowledgments

This work is supported by the Coordinatorship of Konya Technical University’s Scientific Research Projects.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.

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Copyright information

© International Federation for Medical and Biological Engineering 2018

Authors and Affiliations

  1. 1.Electrical & Electronics Engineering Department, Faculty of Engineering and Natural SciencesKonya Technical UniversityKonyaTurkey
  2. 2.Radiology Department, Medicine FacultySelçuk UniversityKonyaTurkey

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