在磁共振成像中，对肾上腺病变的准确分类对于诊断和治疗方案的制定非常重要。医学影像中病灶的检测和分类在很大程度上依赖于几个关键因素，包括专家的经验水平、工作强度和临床医生的疲劳程度。这些因素是诊断过程准确性和有效性的关键决定因素，进而直接影响患者的健康结果。随着人工智能技术的普及，计算机辅助诊断系统在疾病诊断中的应用也越来越多。本研究使用深度学习在磁共振成像中对肾上腺病变进行分类，数据集来自塞尔丘克大学医学院放射科，所有肾上腺病变都经过两名有丰富腹部磁共振成像诊断经验的放射科医师共同鉴定和评估。研究使用T1加权和T2加权成像产生的两个不同数据集进行。每种模式下的数据集包括112个良性病变和10个恶性病变。实验通过不同大小的感兴趣区域（ROI）进行，以提高工作性能，并评估所选ROI大小对分类性能的影响。此外，本研究提出了一种名为Abdomen Caps的独特分类模型结构，代替了深度学习中使用的卷积神经网络（CNN）模型。当用于分离训练、验证和测试的数据集不同时，每个阶段得到不同的结果。为了消除这些差异，本研究采用十倍交叉验证。最好的结果为0.982、0.999、0.969、0.983、0.998和0.964，分别对应准确性、精度、召回率、F1分数、曲线下面积（AUC）得分和kappa得分。© 2023。澳大利亚物理科学和医学工程学院。

Accurate classification of adrenal lesions on magnetic resonance (MR) images are very important for diagnosis and treatment planning. The detection and classification of lesions in medical imaging heavily rely on several key factors, including the specialist's level of experience, work intensity, and fatigue of the clinician. These factors are critical determinants of the accuracy and effectiveness of the diagnostic process, which in turn has a direct impact on patient health outcomes. With the spread of artificial intelligence, the use of computer-aided diagnosis (CAD) systems in disease diagnosis has also increased. In this study, adrenal lesion classification was performed using deep learning on MR images. The data set used was obtained from the Department of Radiology, Faculty of Medicine, Selcuk University, and all adrenal lesions were identified and reviewed in consensus by two radiologists experienced with abdominal MR. Studies were carried out on two different data sets created by T1- and T2-weighted MR images. The data set consisted of 112 benign and 10 malignant lesions for each mode. Experiments were performed with regions of interest (ROIs) of different sizes to increase the working performance. Thus, the effect of the selected ROI size on the classification performance was assessed. In addition, instead of the convolutional neural network (CNN) models used in deep learning, a unique classification model structure called Abdomen Caps was proposed. When the data sets used in classification studies are manually separated for training, validation, and testing, different results are obtained with different data sets for each stage. To eliminate this imbalance, tenfold cross-validation was used in this study. The best results obtained were 0.982, 0.999, 0.969, 0.983, 0.998, and 0.964 for accuracy, precision, recall, F1-score, area under the curve (AUC) score, and kappa score, respectively.© 2023. Australasian College of Physical Scientists and Engineers in Medicine.