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Ramkumar, M.
- Classification of Brain Tumor using Bees Swarm Optimisation
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Authors
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
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: 2025-2030Abstract
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.References
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- 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
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Authors
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
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: 2470-2474Abstract
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
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- 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.
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- IRIS Detection For Biometric Pattern Identification Using Deep Learning
Abstract Views :102 |
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Authors
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
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: 2610-2614Abstract
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 LearningReferences
- 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.
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- An Multi Threshold Model for COVID Patients with Initial Identification of Disease
Abstract Views :96 |
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Authors
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
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: 2831-2836Abstract
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.References
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