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Deep feature extraction and fine κ-nearest neighbour for enhanced human papillomavirus detection in cervical cancer - a comprehensive analysis of colposcopy images.

INTRODUCTION: This study introduces a novel methodology for classifying human papillomavirus (HPV) using colposcopy images, focusing on its potential in diagnosing cervical cancer, the second most prevalent malignancy among women globally. Addressing a crucial gap in the literature, this study highlights the unexplored territory of HPV-based colposcopy image diagnosis for cervical cancer. Emphasising the suitability of colposcopy screening in underdeveloped and low-income regions owing to its small, cost-effective setup that eliminates the need for biopsy specimens, the methodological framework includes robust dataset augmentation and feature extraction using EfficientNetB0 architecture.

MATERIAL AND METHODS: The optimal convolutional neural network model was selected through experimentation with 19 architectures, and fine-tuning with the fine κ-nearest neighbour algorithm enhanced the classification precision, enabling detailed distinctions with a single neighbour.

RESULTS: The proposed methodology achieved outstanding results, with a validation accuracy of 99.9% and an area under the curve (AUC) of 99.86%, with robust performance on test data, 91.4% accuracy, and an AUC of 91.76%. These remarkable findings underscore the effectiveness of the integrated approach, which offers a highly accurate and reliable system for HPV classification.Conclusions: This research sets the stage for advancements in medical imaging applications, prompting future refinement and validation in diverse clinical settings.

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