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ICTACT Journal on Image and Video Processing

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Ramkumar, M.

Classification of Brain Tumor using Bees Swarm Optimisation

Abstract Views :244 | PDF Views:1

Authors

M. Ramkumar ¹, M. Babu ², R. Lakshminarayanan ¹

Affiliations
1 Department of Computer Science and Engineering, Gnanamani College of Technology, IN
2 Department of Computer Science and Engineering, Sri Satya Sai University of Technology and Medical Sciences, IN

Source

ICTACT Journal on Image and Video Processing, Vol 10, No 1 (2019), Pagination: 2025-2030

Abstract

Nowadays, processing the medical image is a most significant diagnostic process. Usually RMI is used to detect the presence of and type of tumor. The following process is very complicated in the brain tumor classification. The treatment of medical images, such as image segmentation, image extraction, and image classification, takes various steps. Various types of properties such as intensity, forms and texture-based features are extracted from a segmented MRI image. The feature selection approach is employed to select a small subset of MRI image features that minimize redundancy and maximize target-related pertinence. This article uses the Bees Swarm Optimization (BSO) for the selection and the Neural Network Classifier to classify the type of tumor in present brain MRI images, and then takes online MRI images which contain brain tumor, then a machine-learning model.

Keywords

Neural Network, ACO, Feature Extraction, Classification, MRI.

Full Text

References

A. Devi, A. Babu, A.K. Menon and R.R. Chaandran, “Brain Tumor Detection”, International Journal of Innovative Technology and Research, Vol. 3, No. 2, pp. 1950-1952, 2015.

Debnath Bhattacharyya and Tai-Hoon Kim, “Brain Tumor Detection using MRI Image Analysis”, Proceedings of International Conference on Ubiquitous Computing and Multimedia Applications, pp. 1-6, 2011.

Y. Zhang and L. Wu, “An MR Brain Images Classifier Via Principal Component Analysis and Kernel Support Vector Machine”, Progress in Electromagnetics Research, Vol. 130, pp. 369-388, 2012.

A.K. Jain, R.P.W. Duin and J. Mao, “Statistical Pattern Recognition: A Review”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 22, No. 1, pp. 31-37, 2000.

K. Deb and A.R. Reddy, “Reliable Classification of Two Class Cancer Data using Evolutionary Algorithms”, BioSystems, Vol. 72, No. 1-2, pp. 111-129, 2003.

J. Kennedy and R. Eberhart, “Particle Swarm Optimization”, Proceedings of International Conference on Neural Networks, pp. 1942-1948, 1995.

S.A. Taie and W. Ghonaim, “CSO-based Algorithm with Support Vector Machine for Brain Tumor Disease Diagnosis”, Proceedings of International Workshops on Pervasive Computing and Communications, pp. 332-343, 2017.

M.A. Majid, A.F.Z. Abidin, N.D.K. Anuar, K.A. Kadiran, M.S. Karis, Z.M. Yusoff, N.H.K. Anuar and Z.I. Rizman, “A Comparative Study on the Application of Binary Particle Swarm Optimization and Binary Gravitational Search Algorithm in Feature Selection for Automatic Classification of Brain Tumor MRI”, Journal of Fundamental and Applied Sciences, Vol. 10, No. 2, pp. 486-498, 2018.

V.P. Gladis, Pushpa Rathi and S. Palan, “Brain Tumor MRI Image Classification with Feature Selection and Extraction using Linear Discriminant Analysis”, Proceedings of International Conference on Computer Vision and Pattern Recognition, pp. 233-238, 2012.

B. Xue, M. Zhang and W.N. Browne, “Particle Swarm Optimization for Feature Selection in Classification: A Multi-Objective Approach”, IEEE Transactions on Cybernetics, Vol. 43, No. 6, pp. 68-73, 2013.

Balasubramanian Vijaya Lakshmi and Vasudev Mohan, “Kernel-based PSO and FRVM: An automatic plant leaf type detection using texture, shape, and color features”, Computers and Electronics in Agriculture, Vol. 125, pp. 99-112, 2016.

N. Zulpe and V. Pawar, “GLCM Textural Features for Brain Tumor Classification”, International Journal of Computer Science, Vol. 9, No. 3, pp. 354-367, 2012.

Atiq Ur Rehman, Aasia Khanum and Arslan Shaukat, “Hybrid Feature Selection and Tumor Identification in Brain MRI Using Swarm Intelligence”, Proceedings of IEEE International Conference on Frontiers of Information Technology, pp. 441-448, 2013.

Rajaguru Harikumar and Sunil Kumar Prabhakar, “Oral Cancer Classification from Hybrid ABC-PSO and Bayesian LDA”, Proceedings of IEEE International Conference on Communication and Electronics Systems, pp. 1-7, 2017.

N.N. Gopal and M. Karnan, “Diagnose Brain Tumor through MRI using Image Processing Clustering Algorithms such as Fuzzy C Means along with Intelligent Optimization Techniques”, Proceedings of IEEE International Conference on Computational Intelligence and Computing Research, pp. 26-34, 2010.

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

G. Jothi and H. Inbarani, “Hybrid Tolerance Rough Set-Firefly based Supervised Feature Selection for MRI Brain Tumor Image Classification”, Applied Soft Computing, Vol. 46, pp. 639-651, 2016.

Classification of Cervical Cancer in Women Using Convolutional Neural Network

Abstract Views :170 | PDF Views:1

Authors

M. Ramkumar ¹, R. Manikandan ², M. Punithavalli ³, V. S. Akshaya4 ⁴, Shanmugaraj Madasamy ⁵

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

Source

ICTACT Journal on Image and Video Processing, Vol 11, No 4 (2021), Pagination: 2470-2474

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.

Full Text

References

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.

IRIS Detection For Biometric Pattern Identification Using Deep Learning

Abstract Views :102 | PDF Views:0

Authors

M. Ramkumar ¹, Amirtha Preeya ², R. Manikandan ³, T. Karthikeyan ⁴

Affiliations
1 Department of Computer Science and Engineering, HKBK College of Engineering, IN
2 Department of Computer Science and Engineering, Presidency University, IN
3 Department of Computer Science, The Quaide Milleth College for Men, IN
4 Department of Electronics and Telecommunications Engineering, University of Technology and Applied Sciences, OM

Source

ICTACT Journal on Image and Video Processing, Vol 12, No 2 (2021), Pagination: 2610-2614

Abstract

In this paper, we develop a liveness detection of iris present in the study to reduce various spoofing attacks using gray-level co-occurrence matrix (GLCM) and Deep Learning (DL). The input images of iris are been given to this technique for the extraction of texture and colour features with fine details. The details are fused finally and given to a DL classifier for the classification of liveness detection. The simulation is conducted to test the efficacy of the model and the results of simulation shows that the proposed method achieves higher level of accuracy than existing methods.

Keywords

Iris Detection, Pattern Identification, Liveness Detection, Biometric, Deep Learning

Full Text

References

Z. Zhao and A. Kumar, “A Deep Learning based Unified Framework to Detect, Segment and Recognize Irises using Spatially Corresponding Features”, Pattern Recognition, Vol. 93, pp. 546-557, 2019.

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.

A. Khadidos, A.O. Khadidos and S. Kannan, “Analysis of COVID-19 Infections on a CT Image using Deep Sense Model”, Frontiers in Public Health, Vol. 8, pp. 1-18, 2020.

K. Srihari, G. Dhiman and S. Chandragandhi, “An IoT and Machine Learning‐based Routing Protocol for Reconfigurable Engineering Application”, IET Communications, Vol. 23, No. 2, pp. 1-15, 2021.

S.B. Sangeetha, R. Sabitha and B. Dhiyanesh, “Resource Management Framework using Deep Neural Networks in Multi-Cloud Environment”, Proceedings of International Conference on Operationalizing Multi-Cloud Environments, pp. 89-104, 2021.

H. Proenca and J.C. Neves, “Deep-Prwis: Periocular Recognition without the Iris and Sclera using Deep Learning Frameworks”, IEEE Transactions on Information Forensics and Security, Vol. 13, No. 4, pp. 888-896, 2017.

H. Proenca and J.C. Neves, “Segmentation-Less and NonHolistic Deep-Learning Frameworks for Iris Recognition”, Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, pp. 1-8, 2019.

N.V. Kousik and M. Saravanan, “A Review of Various Reversible Embedding Mechanisms”, International Journal of Intelligence and Sustainable Computing, Vol. 1, No. 3, pp. 233-266, 2021.

I.J. Jacob, “Capsule Network based Biometric Recognition System”, Journal of Artificial Intelligence, Vol. 1, No. 2, pp. 83-94, 2019.

M. Vatsa, R. Singh and A. Majumdar, “Deep Learning in Biometrics”, CRC Press, 2018.

V. Maheshwari, M.R. Mahmood, S. Sravanthi and N. Arivazhagan, “Nanotechnology-Based Sensitive Biosensors for COVID-19 Prediction Using Fuzzy Logic Control”, Journal of Nanomaterials, Vol. 2021, pp. 1-14, 2021.

S. Umer, A. Sardar and B.C. Dhara, “Person Identification using Fusion of Iris and Periocular Deep Features”, Neural Networks, Vol. 122, pp. 407-419, 2020.

S. Arora and M.P.S. Bhatia, “Presentation Attack Detection for Iris Recognition using Deep Learning”, International Journal of System Assurance Engineering and Management, Vol. 8, No. 2, pp. 1-7, 2020.

An Multi Threshold Model for COVID Patients with Initial Identification of Disease

Abstract Views :96 | PDF Views:1

Authors

M. Ramkumar ¹, V. Amirtha Preeya ², V. Sathiyapriya ³, M. Marimuthu ⁴

Affiliations
1 Department of Computer Science and Business Systems, Knowledge Institute of Technology, IN
2 Department of Computer Science Engineering, Presidency University, IN
3 Department of Computer Science and Engineering, Knowledge Institute of Technology, IN
4 Department of Computer Science and Engineering, Sona College of Technology, IN

Source

ICTACT Journal on Image and Video Processing, Vol 13, No 2 (2022), Pagination: 2831-2836

Abstract

Many strains of corona virus such as alpha, beta, gamma, delta, and omicron are still prevalent in various parts of the world. The new type of corona virus is called a variant when it is caused by more than one genetic mutation from the previous type of corona virus. Various strains around the world have come so far. The cough may persist for more than an hour or three or four times in 24 hours and body heat is high. You may not be able to feel the smell or taste. Researchers say that some people may have symptoms similar to those of a severe cold. In this paper, a multi threshold model was proposed to find the initial infection detection of COVID disease. Based on the initial health symptoms these methods observe the inputs of the patients. Then the observed symptoms are compared with the existing database and identify the spreading of the disease. This report was directly monitored by the patient and doctor. This model was helpful to provide the periodical monitoring and perfect treatments to the infected patients.Many strains of corona virus such as alpha, beta, gamma, delta, and omicron are still prevalent in various parts of the world. The new type of corona virus is called a variant when it is caused by more than one genetic mutation from the previous type of corona virus. Various strains around the world have come so far. The cough may persist for more than an hour or three or four times in 24 hours and body heat is high. You may not be able to feel the smell or taste. Researchers say that some people may have symptoms similar to those of a severe cold. In this paper, a multi threshold model was proposed to find the initial infection detection of COVID disease. Based on the initial health symptoms these methods observe the inputs of the patients. Then the observed symptoms are compared with the existing database and identify the spreading of the disease. This report was directly monitored by the patient and doctor. This model was helpful to provide the periodical monitoring and perfect treatments to the infected patients.

Keywords

Alpha, Beta, Gamma, Delta, Omicron, COVID, Threshold Model.

Full Text

References

A. Fakhry and A. Khanzada, “Virufy: A Multi-Branch Deep Learning Network for Automated Detection of COVID-19”, Proceedings of International Conference on Deep Learning, pp. 1-6, 2021.

M. Loey and S. Mirjalili, “COVID-19 Cough Sound Symptoms Classification from Scalogram Image Representation using Deep Learning Models”, Computers in Biology and Medicine, Vol. 139, pp. 105020-105026, 2021.

K.L. Narayanan and V. Saravanan, “Banana Plant Disease Classification using Hybrid Convolutional Neural Network”, Computational Intelligence and Neuroscience, Vol. 2022, pp. 1-14, 2022.

M. Kavitha and B. Twala, “Covid-19 Disease Diagnosis using Smart Deep Learning Techniques”, Journal of Applied Science and Engineering, Vol. 24, No. 3, pp. 271-277, 2021.

V. Saravanan and S. Hariprasath, “Diabetic Retinopathy Feature Extraction and Classification using Adaptive Super Pixel Algorithm”, International Journal on Engineering Advance Technology, Vol. 9, pp. 618-627, 2019.

V. Madaan, A. Roy and R. Prodan, “XCOVNet: Chest X-Ray Image Classification for COVID-19 Early Detection using Convolutional Neural Networks”, New Generation Computing, Vol. 39, No. 3, pp. 583-597, 2021.

T. Alafif and S. Zia, “Machine and Deep Learning towards COVID-19 Diagnosis and Treatment: Survey, Challenges, and Future Directions”, International Journal of Environmental Research and Public Health, Vol. 18, No. 3, pp. 1117-1123, 2021.

J. Logeshwaran, M. Ramkumar, T. Kiruthiga and Sharan Pravin Ravi, “SVPA - The Segmentation based Visual Processing Algorithm (SVPA) for Illustration Enhancements in Digital Video Processing (DVP)” ICTACT Journal on Image and Video Processing, Vol. 12, No. 3, pp. 2669-2673, 2022.

A. Khadidos and G. Tsaramirsis, “Analysis of Covid-19 Infections on a CT Image using Deepsense Model”, Frontiers in Public Health, Vol. 8, pp. 599550-599567, 2020.

S. Fouladi and A. Ahmadian, “Efficient Deep Neural Networks for Classification of COVID-19 based on CT Images: Virtualization via Software Defined Radio”, Computer Communications, Vol. 176, pp. 234-248, 2021.

K. Srihari, S. Chandragandhi 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.

A. Fathan and W.H. Kang, “An Ensemble Approach for the Diagnosis of COVID-19 from Speech and Cough Sounds”, Proceedings of International Conference on Speech and Computer, pp. 190-201, 2021.

V. Maheshwari, A. ParimalaGandhi and V.P. Sundramurthy, “Nanotechnology-Based Sensitive Biosensors for COVID-19 Prediction Using Fuzzy Logic Control”, Journal of Nanomaterials, Vol. 2021, pp. 1-8, 2021.

P. Arulprakash and P. Dhinakar, “Analysis of Convolutional Recurrent Neural Network Classifier for COVID-19 Symptoms over Computerised Tomography Images”, International Journal of Computer Applications in Technology, Vol. 66, No. 3-4, pp. 427-432, 2021,

S.S. Stevens and E.B. Newman, “The Mel Scale Equates the Magnitude of Perceived Differences in Pitch at Different Frequencies”, Journal of the Acoustical Society of America, Vol. 8, No. 3, pp. 185-190, 1937.

B.W. Schuller, “COVID-19 and Computer Audition: An Overview on What Speech and Sound Analysis Could Contribute in theSARS-CoV-2 Corona Crisis”, Frontiers in Digital Health, Vol. 3, pp. 1-14, 2021.

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