To develop a deep learning system that enhances lung cancer classification by effectively integrating spatial correlation among multimodal imaging data.
Key Findings:
Achieved 98% accuracy and 100% recall on malignant tumors, outperforming traditional methods.
Demonstrated improved precision, recall, and F1-score compared to traditional methods, highlighting the effectiveness of spatial correlation.
Model is computationally efficient and applicable in clinical settings, making it a viable option for real-world use.
Interpretation:
The findings underscore the critical role of spatial dependency modeling in enhancing multimodal analysis of medical images for lung cancer diagnosis.
Limitations:
The study may require validation on diverse datasets, including various imaging protocols and patient demographics, to ensure generalizability.
Potential computational resource requirements for real-world implementation may limit accessibility.
Conclusion:
The proposed framework effectively enhances lung cancer diagnosis through spatial correlation, offering a promising approach for clinical applications.
