Neutrosophic Topological Spaces for Lung Cancer Detection in Chest X-Rays: A Novel Approach to Uncertainty Management
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...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
University of New Mexico
2025-03-01
|
| Series: | Neutrosophic Sets and Systems |
| Subjects: | |
| Online Access: | https://fs.unm.edu/NSS/NeutrosophicTopological21.pdf |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849233748896251904 |
|---|---|
| author | A. A. Salama Doaa E. Mossa Mahmoud Y. Shams Huda E. Khalid Ahmed K. Essa |
| author_facet | A. A. Salama Doaa E. Mossa Mahmoud Y. Shams Huda E. Khalid Ahmed K. Essa |
| author_sort | A. A. Salama |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-c331596c1f754e8fb5ad6898db4cdf06 |
| institution | Kabale University |
| issn | 2331-6055 2331-608X |
| language | English |
| publishDate | 2025-03-01 |
| publisher | University of New Mexico |
| record_format | Article |
| series | Neutrosophic Sets and Systems |
| spelling | doaj-art-c331596c1f754e8fb5ad6898db4cdf062025-08-20T04:03:25ZengUniversity of New MexicoNeutrosophic Sets and Systems2331-60552331-608X2025-03-017743244910.5281/zenodo.14172142Neutrosophic Topological Spaces for Lung Cancer Detection in Chest X-Rays: A Novel Approach to Uncertainty ManagementA. A. SalamaDoaa E. MossaMahmoud Y. ShamsHuda E. KhalidAhmed K. EssaDecision-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.https://fs.unm.edu/NSS/NeutrosophicTopological21.pdfneutrosophic setsneutrosophic topological spacesmedical diagnosislung cancer detectionchest x-ray imagesuncertaintydecision-making |
| spellingShingle | A. A. Salama Doaa E. Mossa Mahmoud Y. Shams Huda E. Khalid Ahmed K. Essa Neutrosophic Topological Spaces for Lung Cancer Detection in Chest X-Rays: A Novel Approach to Uncertainty Management Neutrosophic Sets and Systems neutrosophic sets neutrosophic topological spaces medical diagnosis lung cancer detection chest x-ray images uncertainty decision-making |
| title | Neutrosophic Topological Spaces for Lung Cancer Detection in Chest X-Rays: A Novel Approach to Uncertainty Management |
| title_full | Neutrosophic Topological Spaces for Lung Cancer Detection in Chest X-Rays: A Novel Approach to Uncertainty Management |
| title_fullStr | Neutrosophic Topological Spaces for Lung Cancer Detection in Chest X-Rays: A Novel Approach to Uncertainty Management |
| title_full_unstemmed | Neutrosophic Topological Spaces for Lung Cancer Detection in Chest X-Rays: A Novel Approach to Uncertainty Management |
| title_short | Neutrosophic Topological Spaces for Lung Cancer Detection in Chest X-Rays: A Novel Approach to Uncertainty Management |
| title_sort | neutrosophic topological spaces for lung cancer detection in chest x rays a novel approach to uncertainty management |
| topic | neutrosophic sets neutrosophic topological spaces medical diagnosis lung cancer detection chest x-ray images uncertainty decision-making |
| url | https://fs.unm.edu/NSS/NeutrosophicTopological21.pdf |
| work_keys_str_mv | AT aasalama neutrosophictopologicalspacesforlungcancerdetectioninchestxraysanovelapproachtouncertaintymanagement AT doaaemossa neutrosophictopologicalspacesforlungcancerdetectioninchestxraysanovelapproachtouncertaintymanagement AT mahmoudyshams neutrosophictopologicalspacesforlungcancerdetectioninchestxraysanovelapproachtouncertaintymanagement AT hudaekhalid neutrosophictopologicalspacesforlungcancerdetectioninchestxraysanovelapproachtouncertaintymanagement AT ahmedkessa neutrosophictopologicalspacesforlungcancerdetectioninchestxraysanovelapproachtouncertaintymanagement |