Cervical Cancer Detection using Deep Learning and Image Processing Techniques
Main Article Content
Abstract
Cervical cancer is the second most common cancer among women across the globe. Detecting abnormal cervical cells at an early stage is vital for prompt treatment and improved survival rates. This project focuses on developing an effective approach to identify cervical cancer in Pap smear images using modern digital image processing and deep learning techniques. The system begins by preprocessing the medical slides to improve image clarity and reduce noise and then applies segmentation methods to highlight and separate the regions of interest. The significant tasks include preprocessing methods such as resize and normalization; segmentation methods such as DeepLabV3, Otsu and Canny edge detection and feature extraction methods
such as ResNet101, ResNet152, AlexNet, Inceptionv3, and VGGNet16 to extract features of the cell, such as size, texture and shape of the cell, and shape and color of the nuclei. To distinguish between cancerous and noncancerous images, various machine learning algorithms are employed, including Decision Tree, Random Forest, Logistic Regression, and Support Vector Machine (SVM). The proposed methodology is evaluated on the SIPaKMeD dataset, with performance measured using established metrics such as precision, accuracy, recall, specificity, F1-score and harmonic mean to validate its robustness and reliability. By presenting a cost-effective, automated diagnostic framework to support pathologists in early cervical cancer detection, this study aligns with the broader objectives of healthcare innovation. It has the potential to enhance diagnostic efficiency and contribute to improved public health outcomes. Finally, the combination of feature extractor VGGNet 16 and classifier decision tree gave the highest performance.
Article Details
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
This journal provides immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge.
- Creative Commons Copyright License
The journal allows readers to download and share all published articles as long as they properly cite such articles; however, they cannot change them or use them commercially. This is classified as CC BY-NC-ND for the creative commons license.
- Retention of Copyright and Publishing Rights
The journal allows the authors of the published articles to hold copyrights and publishing rights without restrictions.
References
M. Singh, R. P. Jha, N. Shri, K. Bhattacharyya, P. Patel and D. Dhamnetiya, “Secular trends in incidence and mortality of cervical cancer in India and its states, 1990–2019: Data from the Global Burden of Disease 2019 study,” BMC Cancer, Vol. 22, No. 1, p. 149, 2022.
T. Ramamoorthy, V. Kulothungan, K. Sathishkumar, N. Tomy, R. Mohan, S. Balan and P. Mathur, “Burden of cervical cancer in India: Estimates of years of life lost, years lived with disability and disability-adjusted life years at national and subnational levels using the National Cancer Registry Programme data,” Reproductive Health, Vol. 21, No. 1, p. 111, 2024.
A. N. Della Fera, A. Warburton, T. L. Coursey, S. Khurana and A. A. McBride, “Persistent human papillomavirus infection,” Viruses, Vol. 13, No. 2, p. 321, 2021.
X. Zhang, N. Han and J. Zhang, “Comparative analysis of VGG, ResNet and GoogLeNet architectures evaluating performance, computational efficiency and convergence rates,” Applied and Computational Engineering, Vol. 44, pp. 172–181, 2024.
Y. Krishnamoorthy, K. Ganesh and M. Sakthivel, “Prevalence and determinants of breast and cervical cancer screening among women aged 30–49 years in India,” Indian Journal of Cancer, Vol. 59, No. 1, pp. 54–64, 2022.
P. Shanthi and K. Hareesha, “Automated detection and classification of cervical cancer using Pap smear microscopic images: A comprehensive review and future perspectives,” Engineered Science, Vol. 19, No. 4, pp. 20–41, 2022.
S. K. Mathivanan, D. Francis, S. Srinivasan, V. Khatavkar, K. P. and M. A. Shah, “Enhancing cervical cancer detection and robust classification through a fusion of deep learning models,” Scientific Reports, Vol. 14, No. 1, p. 10812, 2024.
D. D. Himabindu, E. L. Lydia, M. Rajesh, M. A. Ahmed and M. K. Ishak, “Leveraging Swin Transformer with ensemble of deep learning models for cervical cancer screening using colposcopy
images,” Scientific Reports, Vol. 15, 2025.
M. Waqar, A. H. Khan and I. Bhatti, “Artificial intelligence in automated healthcare diagnostics: Transforming patient care,” Revista Española de Documentación Científica, Vol. 19, No. 2, pp. 83–103, 2024.
S. Asif, Y. Wenhui, S. ur Rehman, Q. ul Ain, K. Amjad, Y. Yueyang, S. Jinhai and M. Awais, “Advancements and prospects of machine learning in medical diagnostics: Unveiling the future of diagnostic precision,” Archives of Computational Methods in Engineering, pp. 1–31, 2024.
R. Archana and P. E. Jeevaraj, “Deep learning models for digital image processing: A review,” Artificial Intelligence Review, Vol. 57, No. 1, p. 11, 2024.
Z. Loukil, A Hybrid Approach to Intelligent Prediction of Medical Conditions, Ph.D. Thesis, University of Gloucestershire, Gloucestershire, UK, 2024.
M. M. Kalbhor and S. V. Shinde, “Cervical cancer diagnosis using convolution neural network: Feature learning and transfer learning approaches,” Soft Computing, pp. 1–11, 2023.
B. Bhavsar and B. Shrimali, “TransPapCanCervix: An enhanced transfer learning-based ensemble model for cervical cancer classification,” Computational Intelligence, Vol. 41, No. 1, p. e70027, 2025.
R. Thangamani, M. Vimaladevi, S. Varshini, A. M. Aadhil and K. Sasvath, “Cervical cancer prediction using machine learning,” in Proceedings of the 15th International Conference on Computing Communication and Networking Technologies (ICCCNT), IEEE, pp. 1–7, 2024.
B. Kaushik, A. Mahajan, A. Chadha, Y. F. Khan and S. Sharma, “A fine-tuned adaptive weight deep dense meta stacked transfer learning model for effective cervical cancer prediction,” Physica
Scripta, 2025.
G. Sambasivam, G. Prabu Kanna, M. S. Chauhan, P. Raja and Y. Kumar, “A hybrid deep learning model approach for automated detection and classification of cassava leaf diseases,” Scientific Reports, Vol. 15, No. 1, p. 7009, 2025.
M. Salmi, D. Atif, D. Oliva, A. Abraham and S. Ventura, “Handling imbalanced medical datasets: Review of a decade of research,” Artificial Intelligence Review, Vol. 57, No. 10, p. 273, 2024.
P. Shourie, V. Anand and S. Gupta, “Bridging nature and technology: ResNet-152 for scene visualization and classification,” in Proceedings of the 4th International Conference on Sustainable Expert Systems (ICSES), IEEE, pp. 1512–1516, 2024.
L. Liu, J. Liu, Q. Su, Y. Chu, H. Xia and R. Xu, “Performance of artificial intelligence for diagnosing cervical intraepithelial neoplasia and cervical cancer: A systematic review and meta-analysis,” EClinicalMedicine, Vol. 80, 2025.
H. D. Vargas-Cardona, M. Rodriguez-Lopez, M. Arrivillaga, C. Vergara-Sanchez, J. P. García- Cifuentes, P. C. Bermúdez and A. Jaramillo-Botero, “Artificial intelligence for cervical cancer screening: Scoping review, 2009–2022,” International Journal of Gynecology and Obstetrics, Vol. 165, No. 2, pp. 566–578, 2024.
A. Choubey, S. B. Choubey and S. Kumar, “VGG network-based deep convoluted facial recognition,” Smart Innovation, Systems and Technologies, 2024.
T. S. R. Kiran, Y. Madhuri, P. V. B. Annapurna, A. Lakshmanarao and B. Murthy, “A novel approach to skin cancer detection using CNN, ResNet and hybrid feature extraction,” 2nd International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS, pp. 217–222, 2024.
H. Kaur, R. Sharma and J. Kaur, “Comparison of deep transfer learning models for classification of cervical cancer from Pap smear images,” Scientific Reports, Vol. 15, No. 1, p. 3945, 2025.
P. Kaur and P. Mahajan, “Detection of brain tumors using a transfer learning-based optimized ResNet152 model in MR images,” Computers in Biology and Medicine, Vol. 188, p. 109790, 2025.
P. S. Thakur, T. Sheorey and A. Ojha, “VGGiCNN: A lightweight CNN model for crop disease identification,” Multimedia Tools and Applications, Vol. 82, No. 1, pp. 497–520, 2023.
A. Boulesnane, L. Bellil and M. G. Ghiri, “A hybrid CNN–Random Forest model with landmark angles for real-time Arabic sign language recognition,” Neural Computing and Applications, Vol. 37, No. 4, pp. 2641–2662, 2025.
E. Halabaku and E. Bytyçi, “Overfitting in machine learning: A comparative analysis of decision trees and random forests,” Intelligent Automation and Soft Computing, Vol. 39, No. 6, 2024.
A. Nazir, A. Hussain, M. Singh and A. Assad, “Deep learning in medicine: Advancing healthcare with intelligent solutions and the future of holography imaging in early diagnosis,” Multimedia Tools and Applications, pp. 1–64, 2024.
S. Adamu, H. Alhussian, N. Aziz, S. J. Abdulkadir, A. Alwadin, A. A. Imam, M. Abdullahi, A. Garba and Y. Saidu, “The future of skin cancer diagnosis: A comprehensive systematic literature review of machine learning and deep learning models,” Cogent Engineering, Vol. 11, No. 1, p. 2395425, 2024.
J. Gupta, S. Pathak and G. Kumar, “Deep learning (CNN) and transfer learning: A review,” Journal of Physics: Conference Series, Vol. 2273, p. 012029, IOP Publishing, 2022.
SIPaKMeD Dataset, University of Ioannina, Available online:
https://www.cs.uoi.gr/marina/SIPAKMED/, Accessed April 14, 2025.
S. Zolfaghari, T. Yousefi Rezaii and S. Meshgini, “Applying common spatial pattern and convolutional neural network to classify movements via EEG signals,” Clinical EEG and Neuroscience, Vol. 55, No. 4, pp. 486–495, 2024.
H. M. El-Hoseny, H. F. Elsepae, W. A. Mohamed and A. S. Selmy, “Optimized deep learning approach for efficient diabetic retinopathy classification combining VGG16-CNN,” Computers, Materials and Continua, Vol. 77, No. 2, 2023.
R. Preethi, R. Sudharsan, P. Swetha, C. Rajasekaran and S. Kausalya, “Deep learningbased semantic segmentation of turmeric crop images using the DeepLabV3+ architecture,” in
Proceedings of the 2nd International Conference on Artificial Intelligence and Machine Learning Applications (AIMLA), IEEE, pp. 1–6, 2024.
