A Machine Learning Pipeline for Adenoma Detection in MRI: Integrating Deep Learning and Ensemble Classification

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.