Adrenal tumors occur on adrenal glands and are generally detected on abdominal area scans. Adrenal tumors, which are incidentally detected, release vital hormones. These types of tumors that can be malignant affect body metabolism. Both of benign and malign adrenal tumors can have a similar size, intensity, and shape, this situation may lead to wrong decision during diagnosis and characterization of tumors. Thus, biopsy is done to confirm diagnosis of tumor types. In this study, adrenal tumor characterization is handled by using magnetic resonance images. In this way, it is wanted that patient can be disentangled from one or more imaging modalities (some of them can includes X‐ray) and biopsy. An adrenal tumor image set, which includes five types of adrenal tumors and has 112 benign tumors and 10 malign tumors, was used in this study. Two data sets were created from the adrenal tumor image set by manually/semiautomatically segmented adrenal tumors and feature sets of these data sets are constituted by different methods. Two‐dimensional gray‐level co‐occurrence matrix (2D‐GLCM), gray‐level run‐length matrix (GLRLM), and two‐dimensional discrete wavelet transform (2D‐DWT) methods were analyzed to reveal the most effective features on adrenal tumor characterization. Feature sets were classified in two ways: benign/malign (binary classification) and type characterization (multiclass classification). Support vector machine and artificial neural network classified feature sets. The best performance on benign/malign classification was obtained by the 2D‐GLCM feature set. The best results were assessed with sensitivity, specificity, accuracy, precision, and F‐score metrics and they were 99.17%, 90%, 98.4%, 99.17%, and 99.13%, respectively. The highest classification performance on type characterization was obtained by the 2D‐DWT feature set as 59.62%, 96.17%, 93.19%, 54.69%, and 54.94% for sensitivity, specificity, accuracy, precision, and F‐score metrics, respectively.

中文翻译：

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磁共振图像上的肾上腺肿瘤特征

肾上腺肿瘤发生在肾上腺上，通常在腹部扫描中检测到。偶然发现的肾上腺肿瘤会释放重要的激素。这些可能是恶性的肿瘤类型会影响身体新陈代谢。良性和恶性肾上腺肿瘤的大小、强度和形状可能相似，这种情况可能会导致在肿瘤的诊断和表征过程中做出错误的决定。因此，进行活检以确认肿瘤类型的诊断。在这项研究中，肾上腺肿瘤特征是通过使用磁共振图像处理的。通过这种方式，希望患者可以从一种或多种成像方式（其中一些可以包括 X 射线）和活检中分离出来。本研究使用了一个肾上腺肿瘤图像集，其中包括五种类型的肾上腺肿瘤，并且有 112 个良性肿瘤和 10 个恶性肿瘤。从手动/半自动分割的肾上腺肿瘤的肾上腺肿瘤图像集创建两个数据集，这些数据集的特征集由不同的方法构成。分析了二维灰度共生矩阵 (2D-GLCM)、灰度游程矩阵 (GLRLM) 和二维离散小波变换 (2D-DWT) 方法，以揭示肾上腺的最有效特征。肿瘤表征。特征集以两种方式分类：良性/恶性（二元分类）和类型表征（多类分类）。支持向量机和人工神经网络分类特征集。2D-GLCM 特征集获得了良性/恶性分类的最佳性能。最好的结果是通过灵敏度、特异性、准确度、精密度、和 F 分数指标，分别为 99.17%、90%、98.4%、99.17% 和 99.13%。2D-DWT 特征集在类型表征上的最高分类性能分别为 59.62%、96.17%、93.19%、54.69% 和 54.94%，用于灵敏度、特异性、准确度、精确度和 F-score 指标。