Background/Objectives: In resource-limited settings, where access to advanced imaging modalities such as magnetic resonance imaging (MRI) and histopathological confirmation may be limited, differentiating between hepatic cysts and metastatic lesions based solely on computed tomography (CT) remains challenging. This limitation may affect diagnostic confidence and increase the risk of misclassification, potentially impacting clinical decision making and patient management. In this study, we aimed to explore a more direct and automated approach for classifying hepatic lesions from CT images. Methods: We developed a deep learning-based framework combining transfer learning, decision fusion, and a stacking strategy by integrating five CNN architectures. The study included 100 patients, equally divided between metastatic liver tumors and pathological hepatic cysts. The dataset was built from both public data (LiTS) and internal clinical cases, and then split into training and testing sets. Results: The proposed stacking model provided the most consistent results, reaching an accuracy of 0.98, with high precision and sensitivity. The improvements in individual models, although moderate, were observed across all evaluation metrics. Conclusions: Overall, this approach offers a practical and reliable way to classify hepatic lesions with minimal manual intervention. It may help improve consistency in diagnosis and could be integrated into clinical workflows to support decision making in low-resource areas.