Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.13091/938
Title: An extensive study for binary characterisation of adrenal tumours
Authors: Koyuncu, Hasan
Ceylan, Rahime
Asoğlu, Semih
Cebeci, Hakan
Koplay, Mustafa
Keywords: Adrenal Tumours
Computed Tomography
Hybrid Classifier
Optimisation
Tumour Classification
Feature-Extraction
Classification
Diagnosis
Mri
Issue Date: 2019
Publisher: SPRINGER HEIDELBERG
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.
URI: https://doi.org/10.1007/s11517-018-1923-z
https://hdl.handle.net/20.500.13091/938
ISSN: 0140-0118
1741-0444
Appears in Collections:Mühendislik ve Doğa Bilimleri Fakültesi Koleksiyonu
PubMed İndeksli Yayınlar Koleksiyonu / PubMed Indexed Publications Collections
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collections
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collections

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