Ovarian cancer (OC) often goes undetected until advanced stages due to mild early symptoms.
Methods:
This research proposes a novel methodology for assessing OC severity through histopathological image analysis, utilizing Rank-Based Leaf in Wind Optimization and Alpha Piecewise Linear Fuzzy techniques. It enhances tissue image quality through normalization and Contrast Limited Adaptive Histogram Equalization, employs ResNet 50 with Inception v4 for feature extraction, and uses a ranking layer to prioritize key features.
Results:
The model achieved 99.25% accuracy and 97.98% precision, effectively classifying tumor severity levels under diagnostic uncertainty.
Conclusion:
This robust approach enhances diagnostic accuracy, supports early detection, and improves treatment planning. Future work will explore cross-validation, model pruning, and real-time integration for clinical applications.
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