<p>This study introduces an innovative approach for the early diagnosis of ovarian cancer by integrating fuzzy rough sets with deep learning techniques. Our methodology preprocesses medical data using fuzzy rough sets to address uncertainty and imprecision, thereby enhancing its suitability for deep learning models. Subsequently, we utilized advanced convolutional neural networks (CNNs) to achieve efficient and accurate diagnosis. The experimental findings reveal that our fuzzy rough convolutional neural network (FRCNN) model attains a perfect classification accuracy of 100%, along with a sensitivity of 1.00 and a specificity of 1.00. This performance markedly outperforms conventional diagnostic approaches and individual machine learning methods. These findings highlight the robustness and effectiveness of our approach in managing the complexities of medical data, resulting in superior diagnostic performance. Despite challenges such as the limited availability of large, annotated datasets and the inherent complexity of medical data, our approach shows great promise for personalized medicine. By equipping clinicians with a powerful tool for early ovarian cancer detection, this method has the potential to improve patient outcomes through earlier and more accurate diagnoses.</p>