###### Abstract  
Adrenal lesions are common findings in abdominal imaging, with adrenal adenomas being the most frequent type. Accurate detection of adrenal adenomas is essential to avoid unnecessary diagnostic procedures and treatments. However, conventional imaging-based evaluation relies heavily on the expertise of radiologists and can be complicated by pseudo-lesions, overlapping imaging features, and suboptimal imaging techniques. To address these challenges, we propose an end-to-end machine learning pipeline that integrates deep learning-based lesion detection (FCOS) with an ensemble classifier for adrenal lesion classification in MRI. Our pipeline operates directly on broader regions of interest, eliminating the need for manual lesion segmentation. Our method was evaluated on a multi-sequence MRI dataset comprising 206 adenomas and 45 non-adenomas. The pipeline achieved 87.45% accuracy, 87.33% specificity, and 87.63% recall for adenoma classification, demonstrating competitive performance compared to prior studies. The results highlight strong non-adenoma identification while maintaining robust adenoma detection. Future research should focus on dataset expansion, external validation, and comparison with radiologist performance to further validate clinical applicability.  
deep learning; ensemble learning; radiomics; MRI; adenoma detection; adrenal lesions 2025154  
presence of pseudo-lesions, overlap of imaging features of different lesions, or incorrect choice of the imaging technique [10]. Approximately 70-80% of adrenal adenomas are lipid-rich, making them easier to detect using imaging techniques sensitive to intracytoplasmic lipid content. However, lipid-poor adenomas pose a greater diagnostic challenge due to substantial overlap in appearance with malignant lesions [4]. Conversely, some benign and malignant lesions with high lipid content can mimic lipid-rich adenomas, further complicating accurate differentiation [11].  
Traditional machine learning (ML) and deep learning (DL) have both made significant contributions to medical imaging analysis. Traditional machine learning methods rely on feature extraction, often requiring domain-specific expertise to identify relevant features in medical images [12; 13]. In contrast, DL, particularly through convolutional neural networks (CNNs), automates feature extraction by learning hierarchical representations directly from raw image data. This automation has resulted in notable improvements in accuracy and efficiency for tasks such as segmentation, classification, and detection of various medical conditions [14; 15; 16; 17]. However, DL faces data quality and availability challenges, as collecting sufficient data and creating high-quality annotations remain time-intensive tasks that require significant effort from medical experts [18]. In the context of adrenal lesion analysis, most applications to date have utilized legacy machine learning models that rely on radiomic features to classify adrenal lesions [9].  
In this study, we aimed to develop a machine learning pipeline that integrates a DL model for adrenal lesion detection with a traditional ML model for classifying the radiomic features of the detected lesions. The object detection DL model was chosen for its ability to learn hierarchical features directly from raw magnetic resonance imaging (MRI) images, enabling precise and efficient lesion detection. Once detected, the identified lesions are analyzed through an ensemble learning model, which classifies them based on their radiomic features. This combination creates a robust end-to-end framework for adrenal lesion analysis, focusing on adenoma detection.