Ovarian cancer detection from mutual information-ranked clinical biomarkers using an explainable attention-based...

Ovarian cancer is a major health concern for women, contributing to substantial mortality and morbidity. Timely identification of ovarian cancer is crucial for enhancing patient health and survival rates. Current diagnostic practices involve the manual analysis of various clinical biomarkers to detect ovarian cancer. However, this approach can be subjective, time-consuming, and dependent on the expertise of the medical professional. To optimize workflow efficiency and improve diagnosis accuracy, we develop an automated deep learning model, called EA-ResMLP, which integrates a residual multilayer perceptron with squeeze-and-excitation attention block and explainable artificial intelligence. The integration of residual connections and attention mechanisms contributes to improved diagnostic accuracy by enabling deeper feature learning and emphasizing the most informative features through adaptive recalibration. The experimental results demonstrated that proposed method achieved an accuracy of 92.05%, indicating a 7.98% improvement over the conventional multilayer perceptron. Furthermore, the predictions of the EA-ResMLP model are analyzed using explainable artificial intelligence techniques such as local interpretable model-agnostic explanations, which generate feature contribution charts to highlight the impact of each input feature on the prediction. By integrating model predictions with feature contribution charts, the proposed model provides an explainable framework for ovarian cancer detection.