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

Design and Analysis on Medical Image Classification for Dengue Detection using Artificial Neural Network Classifier


Affiliations
1 Department of Computer Science and Engineering, Government College of Engineering, Thirssur, India
2 Department of Computer Science and Engineering, IES College of Engineering, India
     

   Subscribe/Renew Journal


Dengue 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 dengue. In this paper, we develop a quick response system that certainly finds the disease through a faster validation process. The study uses artificial neural network (ANN) as a deep learning model that classifies and predicts the condition or the infection status of a patient. The study uses a preprocessing 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 dengue 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, Dengue, 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.

  • 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.

  • 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.

  • Ta-Chien Chan, Tsuey-Hwa Hu, Jing-Shiang Hwang, “Daily Forecast of Dengue Fever Incidents for Urban Villages in a City”, International Journal of Health Geographics, Vol. 14, No. 9, pp. 1-23, 2015.

  • N.K. Kameswara Rao, G.P. Saradhi Varma and M. Nagabhushana Rao, “Classification Rules using Decision Tree for Dengue Disease”, International Journal of Research in Computer and Communication Technology, Vol. 3, No. 3, pp. 1-13, 2014.

  • Veerappan Kousik, N.G, Suresh, K. Patan and A.H. Gandomi, “Improving Power and Resource Management in Heterogeneous Downlink OFDMA Networks”, Information, Vol. 11, No. 4, pp. 203-215, 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.

  • B.G. Cetiner, M. Sari and H.M. Aburas, “Recognition of Dengue Disease Patterns using Artificial Neural Networks”, Proceedings of International Symposium on Advanced Technologies, pp. 359-362, 2009.

  • 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.

  • L.T. Mariappan, “Analysis on Cardiovascular Disease Classification using Machine Learning Framework”, Solid State Technology, Vol. 63, No. 6, pp. 10374-10383, 2020.

  • 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.

  • A.L.V. Gomes, L.J.K. Wee and A.M. Khan, “Classification of Dengue Fever Patients based on Gene Expression Data using Support Vector Machines”, PLoS One, Vol. 5, No. 6, pp. 1-14, 2010.

  • A. Khadidos, A.O. Khadidos, S. Kannan, S.N. Mohanty and G. Tsaramirsis, “Analysis of COVID-19 Infections on a CT Image Using DeepSense Model”, Frontiers in Public Health, Vol. 8, pp. 1-9, 2020.

  • T.M. Carvajal, K.M. Viacrusis, L.F.T. Hernandez, H.T. Ho, D.M. Amalin and K. Watanabe, “Machine Learning Methods Reveal the Temporal Pattern of dengue Incidence using Meteorological Factors in Metropolitan Manila, Philippines”, BMC Infectious Diseases, Vol. 18, No. 1, pp. 183-193, 2018.

  • A. Daniel, B. Kannan and N.V. Kousik, “Predicting Energy Demands Constructed on Ensemble of Classifiers”, Proceedings of International Conference on Intelligent Computing and Applications, pp. 575-583, 2021.

  • K. Kesorn, P. Ongruk and J. Chompoosri, “Morbidity Rate Prediction of Dengue Hemorrhagic Fever (DHF) using the Support Vector Machine and the Aedes Aegypti Infection Rate in Similar Climates and Geographical Areas”, PLoS One, Vol. 10, No. 5, pp. 1-13, 2015.

  • S. Karthick, and P.A. Rajakumari, “Ensemble Similarity Clustering Frame work for Categorical Dataset Clustering using Swarm Intelligence”, Proceedings of International Conference on Intelligent Computing and Applications, pp. 549-557, 2021.

  • 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.


Abstract Views: 222

PDF Views: 0




  • Design and Analysis on Medical Image Classification for Dengue Detection using Artificial Neural Network Classifier

Abstract Views: 222  |  PDF Views: 0

Authors

P. K. Swaraj
Department of Computer Science and Engineering, Government College of Engineering, Thirssur, India
G. Kiruthiga
Department of Computer Science and Engineering, IES College of Engineering, India

Abstract


Dengue 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 dengue. In this paper, we develop a quick response system that certainly finds the disease through a faster validation process. The study uses artificial neural network (ANN) as a deep learning model that classifies and predicts the condition or the infection status of a patient. The study uses a preprocessing 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 dengue 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, Dengue, Classification, Diagnosis.

References