Machine Learning Approaches for Early Detection and Classification of Cervical Cancer Using Image Processing

Traditional machine learning (ML) techniques have limitations that make it difficult for existing algorithms to diagnose cervical cancer. These limitations include lower accuracy and an inability to handle complicated variations in histopathological images. These systems usually rely on manual evaluations, decision trees, logistic regression, and basic neural networks; these methods are not well suited to handling the nuances of precancerous lesions and picture quality fluctuations. By combining innovative techniques, the proposed hybrid system solves these problems. Specifically, it integrates Convolutional Neural Networks (CNNs) for hierarchical feature extraction, Vision Transformers (ViTs) for contextual information and ensemble methods that combine Gradient Boosting Machines (GBMs) and Support Vector Machines (SVMs) for robust classification. Further improving model robustness is self-supervised learning. The methodology outperforms the existing systems in terms of accuracy (97.0%), precision (94.5%), recall (95.2%), and F1-score (94.8<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">%</sup>), indicating improved performance and fewer false positives and negatives.