Version 1
: Received: 4 November 2024 / Approved: 4 November 2024 / Online: 5 November 2024 (09:39:44 CET)
How to cite:
Raza, M. A.; Siddiqui, H. U. R.; Saleem, A. A.; Zafar, K.; Zafar, A.; Dudley, S.; Iqbal, M. Advanced Feature Extraction for Cervical Cancer Image Classification: Integrating Neural Feature Extraction and AutoInt Models. Preprints2024, 2024110203. https://doi.org/10.20944/preprints202411.0203.v1
Raza, M. A.; Siddiqui, H. U. R.; Saleem, A. A.; Zafar, K.; Zafar, A.; Dudley, S.; Iqbal, M. Advanced Feature Extraction for Cervical Cancer Image Classification: Integrating Neural Feature Extraction and AutoInt Models. Preprints 2024, 2024110203. https://doi.org/10.20944/preprints202411.0203.v1
Raza, M. A.; Siddiqui, H. U. R.; Saleem, A. A.; Zafar, K.; Zafar, A.; Dudley, S.; Iqbal, M. Advanced Feature Extraction for Cervical Cancer Image Classification: Integrating Neural Feature Extraction and AutoInt Models. Preprints2024, 2024110203. https://doi.org/10.20944/preprints202411.0203.v1
APA Style
Raza, M. A., Siddiqui, H. U. R., Saleem, A. A., Zafar, K., Zafar, A., Dudley, S., & Iqbal, M. (2024). Advanced Feature Extraction for Cervical Cancer Image Classification: Integrating Neural Feature Extraction and AutoInt Models. Preprints. https://doi.org/10.20944/preprints202411.0203.v1
Chicago/Turabian Style
Raza, M. A., Sandra Dudley and Muhammad Iqbal. 2024 "Advanced Feature Extraction for Cervical Cancer Image Classification: Integrating Neural Feature Extraction and AutoInt Models" Preprints. https://doi.org/10.20944/preprints202411.0203.v1
Abstract
Cervical cancer remains a significant global public health challenge, affecting over half a million women annually, with a mortality rate of approximately 60%, especially in resource-limited regions. This study presents an advanced methodology for cervical cancer diagnosis through deep learning techniques. Utilizing a publicly available cervical cancer image dataset, the research introduces a novel classification framework that integrates a Neural Feature Extractor (NFE) based on a pre-trained VGG16 architecture and an AutoInt model for automatic feature interaction learning. The extracted features are processed through machine learning classifiers such as KNN, LGBM, Extra Trees, and others for classification tasks. Among these classifiers, KNN achieved the highest accuracy of 99.96%, followed closely by LGBM at 99.92%. The study also assesses the computational complexity of various models, demonstrating that simpler models like LDA exhibit faster prediction times, while more complex models, such as KNN and LGBM, provide higher accuracy. These findings highlight the potential of deep learning frameworks in improving cervical cancer classification accuracy, especially in resource-limited environments.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
