ICTACT Journal on Image and Video Processing

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Classification of Cervical Cancer in Women Using Convolutional Neural Network


Affiliations
1 Department of Computer Science and Engineering, Gnanamani College of Technology, India
2 Department of Computer Science, The Quaide Milleth College for Men, India
3 Department of Mechanical Engineering, Rathinam Technical Campus, India
4 Department of Computer Science and Engineering, Sri Eshwar College of Engineering, India
5 Department of Computer Science, Cork Institute of Technology, Ireland
     

   Subscribe/Renew Journal


Cervical cancer is regarded as a serious threats to humanity, globally and this is a vital disease with huge spreading of virus that affects the health of humans. The virus is spreading at a rapid rate through mosquitoes that even may kill the one who is affected with cervical cancer. In this paper, we develop a quick response system that certainly finds the disease through a faster validation process. The study uses Convolutional Neural Network (CNN) as a deep learning model that classifies and predicts the condition or the infection status of a patient. The study uses a pre-processing model and a feature extraction model to prepare the image datasets for classification. The simulation is conducted to validate the effectiveness of the model over cervical cancer image datasets i.e. the blood samples of humans. The validation shows that the proposed method effectively classifies the patients in a faster manner than the other deep learning models.

Keywords

Machine Learning, Cervical Cancer, Classification, Diagnosis.
Subscription Login to verify subscription
User
Notifications
Font Size

  • M. Guzman and G. Kouri, “Dengue and Dengue Hemorrhagic Fever in the Americas: Lessons and Challenges”, Journal of Clinical Virology, Vol. 27, No. 1, pp. 1-13, 2003.

  • S. Kannan and S.N. Mohanty, “Survey of Various Statistical Numerical and Machine Learning Ontological Models on Infectious Disease Ontology”, Proceedings of International Conference on Data Analytics in Bioinformatics: A Machine Learning Perspective, pp. 431-442, 2021.

  • N. Kousik, A. Kallam, R. Patan and A.H. Gandomi, “Improved Salient Object Detection using Hybrid Convolution Recurrent Neural Network”, Expert Systems with Applications, Vol. 166, pp. 114064-114075, 2021.

  • N.V. Kousik, “Analyses on Artificial Intelligence Framework to Detect Crime Pattern”, Proceedings of International Conference on Intelligent Data Analytics for Terror Threat Prediction: Architectures, Methodologies, Techniques and Applications, 119-132, 2021.

  • K. Srihari, S. Chandragandhi, G. Dhiman and A. Kaur, “Analysis of Protein-Ligand Interactions of SARS-Cov-2 Against Selective Drug using Deep Neural Networks”, Big Data Mining and Analytics, Vol. 4, No. 2, pp. 76-83, 2021.

  • K.M. Baalamurugan and S.V. Bhanu, “An Efficient Clustering Scheme for Cloud Computing Problems using Metaheuristic Algorithms”, Cluster Computing, Vol. 22, No. 5, pp. 12917-12927, 2019.

  • T. Karthikeyan and K. Praghash, “An Improved Task Allocation Scheme in Serverless Computing Using Gray Wolf Optimization (GWO) Based Reinforcement Learning (RIL) Approach”, Wireless Personal Communications, Vol. 80, No. 7, 1-19, 2020.

  • J.L. San Martín, J.O. Solorzano and M.G. Guzman, “The Epidemiology of Dengue in the Americas over the Last Three Decades: A Worrisome Reality”, American Journal of Tropical Medicine and Hygiene, Vol. 82, No. 1, pp. 128-135, 2010.

  • K. Srihari, G. Dhiman, K. Somasundaram and M. Masud, “Nature-Inspired-Based Approach for Automated Cyberbullying Classification on Multimedia Social Networking”, Mathematical Problems in Engineering, Vol. 2021, pp. 1-18, 2021.

  • D.A. Thitiprayoonwongse, P.R. Suriyaphol and N.U. Soonthornphisaj, “Data Mining of Dengue Infection using Decision Tree”, Proceedings of International Conference on Latest Advances in Information Science and Applications, pp. 1-14, 2012.

  • V. Nandini and R. Sriranjitha, “Dengue Detection and Prediction System using Data Mining with Frequency Analysis”, Proceedings of International Conference on Computer Science and Information Technology, pp. 1-12, 2016.

  • G. Li, X. Zhou and J. Liu, “Comparison of Three Data Mining Models for Prediction of Advanced Schistosomiasis Prognosis in the Hubei Province”, PLoS Neglected Tropical Diseases, Vol. 12, pp. 1-22, 2018.

  • V. Chang, B. Gobinathan, A. Pinagapani and S. Kannan, “Automatic Detection of Cyberbullying using Multi-Feature Based Artificial Intelligence with Deep Decision Tree Classification”, Computers and Electrical Engineering, Vol. 92, pp. 107186-107198, 2021.

  • A. Shukla, G. Kalnoor and A. Kumar, “Improved Recognition Rate of Different Material Category using Convolutional Neural Networks”, Materials Today: Proceedings, Vol. 78, No. 1, pp. 1-5, 2021.

  • S. Kannan, G. Dhiman and M. Gheisari, “Ubiquitous Vehicular Ad-Hoc Network Computing using Deep Neural Network with IoT-Based Bat Agents for Traffic Management”, Electronics, Vol. 7, No. 1, pp. 785-793, 2021.

  • J. Gowrishankar, T. Narmadha and M. Ramkumar, “Convolutional Neural Network Classification On 2d Craniofacial Images”, International Journal of Grid and Distributed Computing, Vol. 13, No. 1, pp. 1026-1032, 2020.

  • A. Khadidos, A.O. Khadidos and G. Tsaramirsis, “Analysis of COVID-19 Infections on a CT Image using DeepSense Model”, Frontiers in Public Health, Vol. 8, pp. 1-12, 2020.

  • Siriyasatien Padet,Atchara Phumee, Phatsavee Ongruk, Katechan Jampachaisri and Kraisak Kesorn, “Analysis of Significant Factors for Dengue Fever Incidence Prediction”, BMC Bioinformatics, Vol. 17, No. 166, pp. 1-22, 2016.

  • P. Vivekanandan, “An Efficient SVM Based Tumor Classification with Symmetry Non-Negative Matrix Factorization using Gene Expression Data”, Proceedings of International Conference on Information Communication and Embedded Systems, pp. 761-768, 2013.

  • A. Daniel and K.M. Baalamurugan, “A Novel Approach to Minimize Classifier Computational Overheads in Big Data using Neural Networks”, Physical Communication, Vol. 42, pp. 101130-101135, 2020.

  • K.M. Baalamurugan and S.V. Bhanu, “A Multi-Objective Krill Herd Algorithm for Virtual Machine Placement in Cloud Computing”, The Journal of Supercomputing, Vol. 76, No. 6, pp. 4525-4542, 2020.

  • I. Kononenko, “Machine Learning for Medical Diagnosis: History, State of the Art and Perspective,” Artificial Intelligence in Medicine, Vol. 23, No. 1, pp. 89-109, 2001.

  • D. Raval, D. Bhatt, M.K. Kumar, V. Parikh and D. Vyas, “Medical Diagnosis System using Machine Learning”, International Journal of Computer Science and Communication, Vol. 7, No. 1, pp. 177-182, 2016.

  • M. Umar, D. Babu, K.M. Baalamurugan and P. Singh, “Automation of Energy Conservation for Nodes in Wireless Sensor Networks”, International Journal of Future Generation Communication and Networking, Vol. 13, No. 3, pp. 1-12, 2020.

  • C. Saravanabhavan, T. Saravanan, D.B. Mariappan, S. Nagaraj and K.M. Baalamurugan, “Data Mining Model for Chronic Kidney Risks Prediction Based on Using NB-CbH”, Proceedings of IEEE International Conference on Advance Computing and Innovative Technologies in Engineering, pp. 1023-1026, 2021.


Abstract Views: 274

PDF Views: 1




  • Classification of Cervical Cancer in Women Using Convolutional Neural Network

Abstract Views: 274  |  PDF Views: 1

Authors

M. Ramkumar
Department of Computer Science and Engineering, Gnanamani College of Technology, India
R. Manikandan
Department of Computer Science, The Quaide Milleth College for Men, India
M. Punithavalli
Department of Mechanical Engineering, Rathinam Technical Campus, India
V. S. Akshaya4
Department of Computer Science and Engineering, Sri Eshwar College of Engineering, India
Shanmugaraj Madasamy
Department of Computer Science, Cork Institute of Technology, Ireland

Abstract


Cervical cancer is regarded as a serious threats to humanity, globally and this is a vital disease with huge spreading of virus that affects the health of humans. The virus is spreading at a rapid rate through mosquitoes that even may kill the one who is affected with cervical cancer. In this paper, we develop a quick response system that certainly finds the disease through a faster validation process. The study uses Convolutional Neural Network (CNN) as a deep learning model that classifies and predicts the condition or the infection status of a patient. The study uses a pre-processing model and a feature extraction model to prepare the image datasets for classification. The simulation is conducted to validate the effectiveness of the model over cervical cancer image datasets i.e. the blood samples of humans. The validation shows that the proposed method effectively classifies the patients in a faster manner than the other deep learning models.

Keywords


Machine Learning, Cervical Cancer, Classification, Diagnosis.

References