eISSN: 1897-4309
ISSN: 1428-2526
Contemporary Oncology/Współczesna Onkologia
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1/2024
vol. 28
 
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abstract:
Original paper

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

Lipsarani Jena
1, 2
,
Santi Kumari Behera
1
,
Srikanta Dash
3
,
Prabira Kumar Sethy
3, 4

  1. Veer Surendra Sai University of Technology, Burla, India
  2. GITA Autonomous College, Bhubaneswar, India
  3. Sambalpur University, India
  4. Guru Ghasidas Vishwavidyalaya, Bilaspur, C.G., India
Contemp Oncol (Pozn) 2024; 28 (1): 37–44
Online publish date: 2024/04/26
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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.

keywords:

cervical cancer, KNN, deep feature, CNN, colposcopy images

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