Neutrosophic Topological Spaces for Lung Cancer Detection in Chest X-Rays: A Novel Approach to Uncertainty Management - A. A. Salama; Doaa E. Mossa; Mahmoud Y. Shams; Huda E. Khalid; Ahmed K. Essa (Neutrosophic Sets and Systems)
Decision-making in medical diagnosis is often hampered by uncertainties due to incomplete, ambiguous, and evolving information. In reviewing the traditional methods for lung cancer detection, we found that crisp and logic values have more difficulties and challenges. These challenges related to the big data analytics, uncertainty values, and the different circumstances that make it harder for prediction. In this work, we propose a novel approach that use a Neutrosophic Topological Spaces (NTS) for the lung cancer detection in the chest X-ray images. Furthermore, the proposed NTS leverage the strengths points of Neutrosophic Sets (NS) which include the degrees of truth (T), indeterminacy (I), and falsity (F). The proposed model provides more informative results about the uncertainty cases compared with the traditional methods. The results indicated that the proposed NTS approach achieved highest accuracy reached to 85.5% with a sensitivity 88.2%, specificity 82.1%, and AUC 0.91. which mean that the proposed NTS approach are more reliable and efficient than traditional methods for uncertainty.