Hybrid Neural Convolutional Learning Methodology Design to Predict Cervical Cancer Disease Identification Mechanism

Cervical cancer remains a significant global health challenge due to late-stage diagnosis and limited access to advanced screening. Early detection can significantly improve outcomes. This study presents a Hybrid Neural Convolutional Model that combines Convolutional Neural Networks (CNNs) for spatial feature extraction and Long Short-Term Memory (LSTM) networks for temporal sequence learning, aimed at enhancing diagnostic accuracy. Using UCI dataset, the model incorporates advanced preprocessing techniques, including noise reduction, correlation-based feature selection, and PCA-driven dimensionality reduction, to isolate critical predictors and reduce overfitting. The proposed model achieves an outstanding accuracy of 98.08%, significantly outperforming nine established approaches, including Logistic Regression, SVM, Decision Tree, Random Forest, KNN, CNN, LSTM, CNN+LSTM, and Ensemble methods. This success underscores the synergy of CNN’s spatial recognition capabilities and LSTM’s strength in capturing temporal patterns, demonstrating immense potential for earlier diagnosis and timely interventions in cervical cancer detection. By achieving superior performance, this approach offers a promising tool for improving patient care and survival rates, paving the way for more effective and accessible diagnostic solutions.