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Athinarayanan, S.
- Prevention of Cervical Cancer Disease Based on the Effective Screening Stage
Abstract Views :120 |
PDF Views:3
Authors
Affiliations
1 PSN College of Engineering & Technology, Melathediyoor, IN
1 PSN College of Engineering & Technology, Melathediyoor, IN
Source
Biometrics and Bioinformatics, Vol 4, No 6 (2012), Pagination: 243-246Abstract
The cervix is the lower part of the uterus (womb). It is sometimes called the uterine cervix. The body of the uterus (the upper part) is where a baby grows. The cervix connects the body of the uterus to the vagina (birth canal). The part of the cervix closes to the body of the uterus is called the endocervix. The part next to the vagina is the exocervix (or ectocervix). Cervical cancer is a preventable disease and it is affected by a women’s cervical cell. Early diagnosis and treatment is successful in the majority of cases. This paper will be help to any women who would like to know what screening involves and how a diagnosis of cervical cancer is made.Keywords
Cancer, Causes, Screening, Symptoms, Diagnosis, Colposcopy.
- Classification of Cervical Cancer Cells in Pap Smear Screening Test
Abstract Views :152 |
PDF Views:2
Authors
Affiliations
1 Department of Centre for Information Technology and Engineering, Manonmaniam Sundaranar University, IN
2 Department of Computer Application, Sengamala Thayaar Educational Trust Women’s College, IN
1 Department of Centre for Information Technology and Engineering, Manonmaniam Sundaranar University, IN
2 Department of Computer Application, Sengamala Thayaar Educational Trust Women’s College, IN
Source
ICTACT Journal on Image and Video Processing, Vol 6, No 4 (2016), Pagination: 1234-1238Abstract
Cervical cancer is second topmost cancers among women but also, it was a curable one. Regular smear test can discover the sign of precancerous cell and treated the patient according to the result. However sometimes the detection errors can be occurred by smear thickness, cell overlapping or by un-wanted particles in the smear and cytotechnologists faulty diagnosis. Therefore the reason automatic cancer detection was developed. This was help to increase cancer cell mindfulness, diagnosis accuracy with low cost. This detection process consists of some techniques of the image preprocessing that is segmentation and effective texture feature extraction with SVM classification. Then the Final Classification Results of this proposed technique was compared to the previous classification techniques of KNN and ANN and the result would be very useful to cytotechnologists for their further analysis.Keywords
Cancer, Cervical Cancer, Classification.- Computer Aided Diagnosis for Detection and Stage Identification of Cervical Cancer by Using Pap Smear Screening Test Images
Abstract Views :151 |
PDF Views:3
Authors
Affiliations
1 Department of Centre for Information Technology and Engineering, Manonmaniam Sundaranar University, IN
2 Department of Computer Application, Sengamala Thayaar Educational Trust Women’s College, IN
3 Department of Computer Science and Engineering, PSN College of Engineering and Technology, IN
1 Department of Centre for Information Technology and Engineering, Manonmaniam Sundaranar University, IN
2 Department of Computer Application, Sengamala Thayaar Educational Trust Women’s College, IN
3 Department of Computer Science and Engineering, PSN College of Engineering and Technology, IN
Source
ICTACT Journal on Image and Video Processing, Vol 6, No 4 (2016), Pagination: 1244-1251Abstract
The majority of the women of the world were affected by the disease of cervical cancer. As a result of this disease, their death rate was increase as hasty level. Hence so many number of research people was focused this notion as their research interest and also they have done so many number of solutions for finding this cancer by using some image processing technique and achieved a good results only in advanced and high cost techniques of LBC, biopsy or Colposcopy test Images. Therefore the reason, the authors have chosen this problem and also did not only to find whether the patient is affected by a cancer or not. In addition to the patient was affected by this cancer means and also to identify which severity stage of this disease the patient could be live. Then this work has done in based on the images of low cost pap smear screening test by using various image processing techniques with the help of Computerized Image Processing Software Interactive Data Language (IDL-Image Processing Language). Thus the final reports would be very useful to the pathologists for further analysis.Keywords
Cancer, Cervical Cancer, Nuclei Segmentation, Feature Identification, Classification.- Distributed Load Balancing Algorithm for Wireless Sensor Network
Abstract Views :144 |
PDF Views:1
Authors
Affiliations
1 Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, Tirupati, IN
2 Department of Computer Science and Engineering, Universal College of Engineering and Technology, Vallioor, IN
3 Department of Computer Science, The Madurai Diraviyam Thayumanavar Hindu College, IN
1 Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, Tirupati, IN
2 Department of Computer Science and Engineering, Universal College of Engineering and Technology, Vallioor, IN
3 Department of Computer Science, The Madurai Diraviyam Thayumanavar Hindu College, IN
Source
ICTACT Journal on Communication Technology, Vol 9, No 4 (2018), Pagination: 1908-1912Abstract
A Wireless Sensor Network (WSN) comprises of spatially scattered autonomous sensors to screen physical or natural conditions and to amiably go their information through the system to a Base Station. Grouping is a basic assignment in Wireless Sensor Networks for vitality effectiveness and system quality. Grouping through Central Processing Unit in remote sensor systems is outstanding and being used for quite a while. In this paper, we propose a few procedures that balance the vitality utilization of these hubs and guarantee greatest system lifetime by adjusting the activity stack as similarly as could be expected under the circumstances. Directly grouping through dispersed strategies is being produced for conveying with the issues like system lifetime and vitality. In our work, we connected both concentrated and conveyed k-means clustering calculation in system test system. K-means is a model based algorithm that surrogates between two noteworthy advances, passing on perceptions to groups and processing cluster focuses until the point when a ceasing standard is satisfied. Improved results are accomplished and related which demonstrate that conveyed clustering is compelling than brought together grouping.Keywords
Wireless Sensor Network, Clustering, K-Means, Network Stability.References
- F. Zhao and L.J. Guibas, “Wireless Sensor Networks: An Information Processing Approach”, Morgan Kaufmann Publishers, 2004.
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- Jennifer Yick, Biswanath Mukherjee and Dipak Ghosal, “Wireless Sensor Network Survey”, Computer Networks, Vol. 52, No. 12, pp. 2292-2330, 2008.
- A. Ameer Ahmed Abbasi and Mohamed Younis, “A Survey on Clustering Algorithms for Wireless Sensor Networks”, Computer Communications, Vol. 30, No. 14-15, pp. 2826-2841, 2007.
- S. Bandyopadhyay and E.J. Coyle, “An Energy Efficient Hierarchical Clustering Algorithm for Wireless Sensor Networks”, Proceedings of IEEE 22nd Annual Joint Conference on IEEE Computer and Communications Societies, pp. 1713-1723, 2003.
- O. Younis, M. Krunz and S. Ramasubramanian, “Node Clustering in Wireless Sensor Networks: Recent Developments and Deployment Challenges”, IEEE Networks, Vol. 20, No. 3, pp. 20-25, 2006.
- S.P. Lloyd, “Least-Squares Quantization in PCM”, IEEE Transactions on Information Theory, Vol. 28, No. 2, pp. 129-137, 1982.
- C.R. Lin and M. Gerla, “Adaptive Clustering for Mobile Wireless Networks”, IEEE Journal on Selected Areas in Communications, Vol. 15, No. 7, pp. 1265-1275, 1997.
- I.S. Dhillon and D.S. Modha, “A Data-Clustering Algorithm on Distributed Memory Multiprocessors”, Large-Scale Parallel Data Mining, pp. 245-260, 2000.
- R.O. Duda, P.E. Hart and D.G. Stork, “Pattern Classification”, 2nd Edition, Wiley, 2002.
- P.A. Forero, A. Cano and G.B. Giannakis, “Distributed Feature-based Modulation Classification using Wireless Sensor Networks”, Proceedings of IEEE International Conference on Military Communications, pp. 1-5, 2008.
- P.A. Forero, A. Cano and G.B. Giannakis, “Consensus-Based-Means Algorithm for Distributed Learning using Wireless Sensor Networks”, Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 11-14, 2008.
- P.A. Forero, A. Cano and G.B. Giannakis “Consensus-based Distributed Expectation-Maximization Algorithm for Density Estimation and Classification using Wireless Sensor Networks”, Proceedings of IEEE International Conference on Acoustics, Speech and Signal Processing, pp. 1989-1992, 2008.
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- Seema Shivapur, Suvarna G. Kanakaraddi and A.K. Chikaraddi, “Load Balancing Techniques in Wireless Sensor Networks: A Comparative Study”, International Journal of Emerging Technology in Computer Science and Electronics, Vol. 14, No. 2, pp. 21-28, 2015.
- Dipak Wajgi and Nileshsingh V. Thakur, “Load Balancing based Approach to Improve Lifetime of Wireless Sensor Network”, International Journal of Wireless and Mobile Networks, Vol. 4, No. 4, pp. 12-17, 2012.
- Roshani Talmale, M. Nirupama Bhat and Nita Thakare, “Analysis of Load Balancing and Fault Tolerant Routing Protocol for Wireless Sensor Network”, Helix, Vol. 8, No. 5, pp. 3946-3949, 2018.
- Cervical Cancer Detection and Classification by using Effectual Integration of Directional Gabor Texture Feature Extraction and Hybrid Kernel Based Support Vector Classification
Abstract Views :177 |
PDF Views:0
Authors
Affiliations
1 Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, IN
2 Department of Computer Applications, The MDT Hindu College, IN
3 Department of Computer Science and Engineering, Universal College of Engineering and Technology, IN
1 Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, IN
2 Department of Computer Applications, The MDT Hindu College, IN
3 Department of Computer Science and Engineering, Universal College of Engineering and Technology, IN
Source
ICTACT Journal on Image and Video Processing, Vol 9, No 3 (2019), Pagination: 1935-1939Abstract
Planning of invigorating representation is a troublesome and testing process because of the unpredictability of the images and absence of models of the life systems that thoroughly catches the reasonable expressions in each structure. Cervical malignant growth is one of the noteworthy reasons for death among different kinds of the diseases in women around the world. Genuine and auspicious determination can keep the life to some dimension. Therefore, we have proposed a computerized dependable framework for the analysis of the cervical malignancy utilizing surface highlights and machine learning calculation in Pap smear images, it is extremely advantageous to anticipate disease, likewise expands the dependability of the determination. Proposed framework is a multi-organize framework for cell nucleus extraction and disease finding. To begin with, clamor expulsion is performed in the preprocessing venture on the Pap smear images. Exterior highlights are separated from these demand free Pap smear images. Next period of the proposed framework is classification that depends on these separated highlights, SVM classification is utilized. Over 94% exactness is accomplished by the classification stage, demonstrated that the proposed calculation precision is great at recognizing the disease in the Pap smear images.Keywords
Cervical Cancer, Feature Extraction, DGTF, Classification, Hybrid Kernel SVM.References
- Hayder K. Fatlawi, “Enhanced Classification Model for Cervical Cancer Dataset based on Cost Sensitive Classifier”, International Journal of Computer Techniques, Vol. 4, No. 4, pp. 1-8, 2017.
- S. Athinarayanan, M.V. Srinath and R. Kavitha, “Computer Aided Diagnosis for Detection and Stage Identification of Cervical Cancer by using Pap Smear Screening Test Images”, ICTACT Journal on Image and Video Processing, Vol. 6, No. 4, pp. 1244-1251, 2016.
- M. Martinez, L.E. Sucar, H.G. Acosta and N. Cruz, “Bayesian Model Combination and Its Application to Cervical Cancer Detection”, Proceedings of Ibero-American Conference on Artificial Intelligence, pp. 622-631, 2006.
- G. Sun, S. Li, Y. Cao and F. Lang, “Cervical Cancer Diagnosis based on Random Forest”, International Journal of Performability Engineering, Vol. 13, No. 4, pp. 446-457, 2017.
- S. Athinarayanan and M.V. Srinath, “Classification of Cervical Cancer Cells in PAP SMEA Screening Test”, ICTACT Journal on Image and Video Processing, Vol. 6, No. 4, pp. 1234-1238, 2016.
- S. Aswathy, M.A. Quereshi, B. Kurian and K. Leelamoni, “Cervical Cancer Screening: Current Knowledge and Practice among Women in a Rural Population of Kerala, India”, Indian Journal of Medical Research, Vol. 136, No. 2, pp. 205-210, 2012.
- M. Anousouya Devi, S. Ravi and J.V.S. Punitha, “Detection of Cervical Cancer using the Image Classification Algorithms”, International Journal of Circuit Theory and Applications, Vol. 9, No. 3, pp. 153-166, 2016.
- G. Peng, B. Koyel and R.S. Joe, “Nuclei-Based Features for Uterine Cervical Cancer Histology Image Analysis with Fusion-Based Classification”, IEEE Journal of Biomedical and Health Informatics, Vol. 20, No. 6, pp. 1595-1607, 2016.
- K. Thangavel, P.P. Jaganathan and P.O. Easmi, “Data Mining Approach to Cervical Cancer Patients Analysis using Clustering Technique”, Asian Journal of Information Technology, Vol. 5, No. 4, pp. 413-417, 2006.
- S. Athinarayanan and M.V. Srinath, “Robust and Efficient Diagnosis of Cervical Cancer in Pap Smear Images using Texture Features with RBF and Kernel SVM Classification”, ARPN Journal of Engineering and Applied Sciences, Vol. 11, No. 7, pp. 4504-4515, 2016.
- S. Athinarayanan and M.V. Srinath, “Severity Analysis of Cervical Cancer in PAP SMEAR Images by using EEETCM, ERSTCM and CFE Method based Texture Features and Hybrid Kernel Based Support Vector Machine Classifier”, International Journal of Advanced Research, Vol. 4, No. 11, pp. 2751-2764, 2016.
- S. Athinarayanan and M.V. Srinath, “Effective Detection of Cervical Cancer During Pap Smear Screening Test”, Elysium Journal of Engineering Research and Management, Vol. 1, No. 1, pp. 10-14, 2014.
- S. Athinarayanan and M.V. Srinath, “Multi Class Cervical Cancer Classification by using ERSTCM, EMSD and CFE Methods Based Texture Features and Fuzzy Logic Based Hybrid Kernel Support Vector Machine Classifier”, IOSR Journal of Computer Engineering, Vol. 19, No. 1, pp. 23-34, 2017.
- Effective Image Processing Techniques Based Iris Attendance System
Abstract Views :181 |
PDF Views:0
Authors
Affiliations
1 Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, IN
2 Department of Computer Science and Engineering, Sreenidhi Institute of Science and Technology, IN
3 Department of Computer Science and Engineering, Karunya Institute of Technology and Sciences, IN
1 Department of Computer Science and Engineering, Annamacharya Institute of Technology and Sciences, IN
2 Department of Computer Science and Engineering, Sreenidhi Institute of Science and Technology, IN
3 Department of Computer Science and Engineering, Karunya Institute of Technology and Sciences, IN
Source
ICTACT Journal on Image and Video Processing, Vol 10, No 2 (2019), Pagination: 2071-2075Abstract
In this paper, the iris prediction gives an incipient conception for Biometric benchmark process. Biometrics is the most secure and utilize-cordial benchmark implement. Iris perception technology contains pattern perception and optics method. It identifies an individual person by utilizing their individual physical characteristics. Iris perception system is very wide compared with other biometric systems. The main wholesomeness of iris perception system is its stability and uniqueness that results in a single enrolment for the lifetime. It provides increasingly varies than the fingerprint and the other biometrics systems, where the information cannot be stolen.Keywords
Iris Prediction System, Pattern Analysis, Biometric Authentication, Human Eye.References
- Gajendra Singh Chandel and Ankesh Bhargava, “Identification of People by Iris Recognition”, International Journal of Science and Modern Engineering, Vol. 1, No. 4, pp. 1-12, 2013.
- J. Daugman, “How Iris Recognition Works”, IEEE Transactions on Circuits and Systems for Video Technology, Vol. 14, No. 1, pp. 21-29, 2005.
- Richard Yew Fatt Ng, Yong Hour Tay and Kai Ming Mok, “A Review of Iris Recognition Algorithm”, Proceedings of International Symposium on Information Technology, pp. 26-32, 2008.
- Seifedine Kadry and Khaled Smaili, “A Design and Implementation of a Wireless Iris Recognition Attendance Management System”, Information Technology and Control, Vol. 36, No. 3, pp. 323-329, 2007.
- M. Mattam, S.R.M. Karumuri and S.R. Meda, “Architecture for Automated Student Attendance”, Proceedings IEEE 4th International Conference on Technology for Education, pp. 164-167, 2012.
- Iris Recognition, Available at: http://en.wikipedia.org/wiki/Iris_recognition.
- Ujwalla Gawande, Mukesh Zaveri and Avichal Kapur, “Improving Iris Recognition Accuracy by Score Based Fusion Method”, International Journal of Advancements in Technology, Vol. 1, No. 1, pp. 1-8, 2010.
- R.M. Bolle, J.H. Connell, S. Pankanti, N.K. Ratha and A.W. Senior, “Guide to Biometrics”, Springer, 2003.
- N. Sudha, N.B. Puhan, H. Xia and X. Jiang, “Iris Recognition on Edge Maps”, IET Computer Vision, Vol. 3, No. 1, pp. 1-7, 2009.