Implement ANFIS Classification with PSO Algorithm for MRI Images to Classify Parkinsons Images
Keywords:
Parkinson's disease, MRI images, Adaptive Neuro-Fuzzy Inference Systems, PSOAbstract
Millions suffer from neurodegenerative Parkinson's disease (PD). Effective PD treatment requires early and precise diagnosis. MRI offers brain structural information. Machine learning has improved diagnostic accuracy in medical imaging in recent years. This paper presents a novel method to categorize Parkinson's disease MRI images utilizing the Adaptive Neuro-Fuzzy Inference Systems (ANFIS) classification algorithm with optimization PSO feature Selection and image enhancement. Three main steps are proposed. First, MRI images are enhanced to increase quality and highlight significant features. Preprocessing includes noise removal, contrast improvement, and image sharpening. The next categorization phase uses improved photos. Second, this work presents illness diagnostic machine learning methods with optimization like PSO for feature extraction. Finally, ANFIS classifies MRI images as PD or non-PD. Parkinson's disease (PD) is a complex neurological ailment that needs early diagnosis and treatment. Machine learning can help diagnose PD by examining patient data attributes. This work provides an optimal hybrid model that classifies Parkinson's disease using numerous characteristics and multiclass diagnosis detection techniques. The hybrid model combines machine learning algorithms to boost classification accuracy. Clinical, demographic, and genetic data represent the disease. PD classification uses feature selection to find the most relevant and discriminative features. ANFIS fuzzy rules and parameters are designed for accurate classification. PD MRI scans are used to test the suggested method. Classification performance is measured by accuracy, sensitivity, specificity, and area under the curve. To prove its efficacy, the proposed classification method is compared to others. The findings show that ANFIS classification with image enhancement approaches can classify PD. The proposed MRI-based Parkinson's disease diagnostic method is accurate and sensitive. ANFIS's intelligent decision-making and MRI characteristics increase classification performance.
Downloads
References
R. Sun, G. Wang, Z. Fan, T. Xu and W. Y. Ochieng, "An Integrated Urban Positioning Algorithm Using Matching, Particle Swam Optimized Adaptive Neuro Fuzzy Inference System and a Spatial City Model," in IEEE Transactions on Vehicular Technology, vol. 69, no. 5, pp. 4842-4854, May 2020, doi: 10.1109/TVT.2020.2983220.
F. Haque, M. B. I. Reaz, M. E. H. Chowdhury, F. H. Hashim, N. Arsad and S. H. M. Ali, "Diabetic Sensorimotor Polyneuropathy Severity Classification Using Adaptive Neuro Fuzzy Inference System," in IEEE Access, vol. 9, pp. 7618-7631, 2021, doi: 10.1109/ACCESS.2020.3048742.
B. Aslam, A. Maqsoom, A. H. Cheema, F. Ullah, A. Alharbi and M. Imran, "Water Quality Management Using Hybrid Machine Learning and Data Mining Algorithms: An Indexing Approach," in IEEE Access, vol. 10, pp. 119692-119705, 2022, doi: 10.1109/ACCESS.2022.3221430.
M. A. Kumar and A. J. Laxmi, "Machine Learning Based Intentional Islanding Algorithm for DERs in Disaster Management," in IEEE Access, vol. 9, pp. 85300-85309, 2021, doi: 10.1109/ACCESS.2021.3087914.
P. G. Shynu, V. G. Menon, R. L. Kumar, S. Kadry and Y. Nam, "Blockchain-Based Secure Healthcare Application for Diabetic-Cardio Disease Prediction in Fog Computing," in IEEE Access, vol. 9, pp. 45706-45720, 2021, doi: 10.1109/ACCESS.2021.3065440.
S. Ansari, K. A. Alnajjar, M. Saad, S. Abdallah and A. A. El-Moursy, "Automatic Digital Modulation Recognition Based on Genetic-Algorithm-Optimized Machine Learning Models," in IEEE Access, vol. 10, pp. 50265-50277, 2022, doi: 10.1109/ACCESS.2022.3171909.
Ghosh, P., Paul, A., & Chatterjee, S. (2017). Classification of Parkinson's Disease from Brain MRI Using ANFIS with PSO Algorithm. In Proceedings of the International Conference on Data Engineering and Communication Technology (pp. 307-312). Springer.
Raja, K. B., Nirmal, M., & Sujatha, P. (2016). Parkinson's Disease Diagnosis from Brain MRI Images using ANFIS Classifier with PSO Algorithm. Journal of Medical Imaging and Health Informatics, 6(2), 380-384.
S. Leonori, A. Martino, F. M. F. Mascioli and A. Rizzi, "ANFIS Microgrid Energy Management System Synthesis by Hyperplane Clustering Supported by Neurofuzzy Min–Max Classifier," in IEEE Transactions on Emerging Topics in Computational Intelligence, vol. 3, no. 3, pp. 193-204, June 2019, doi: 10.1109/TETCI.2018.2880815.
Gope, S., & Sengupta, I. (2017). Classification of Parkinson's Disease using ANFIS with PSO and SVM with PSO from Brain MRI Images. In Proceedings of the International Conference on Intelligent Computing and Control Systems (pp. 366-370). IEEE.
B. Al-Naami, H. Fraihat, N. Y. Gharaibeh and A. -R. M. Al-Hinnawi, "A Framework Classification of Heart Sound Signals in PhysioNet Challenge 2016 Using High Order Statistics and Adaptive Neuro-Fuzzy Inference System," in IEEE Access, vol. 8, pp. 224852-224859, 2020, doi: 10.1109/ACCESS.2020.3043290.
Dhabliya, D. (2021c). Designing a Routing Protocol towards Enhancing System Network Lifetime. In Intelligent and Reliable Engineering Systems (pp. 160–163). CRC Press.
R. Sun, G. Wang, Z. Fan, T. Xu and W. Y. Ochieng, "An Integrated Urban Positioning Algorithm Using Matching, Particle Swam Optimized Adaptive Neuro Fuzzy Inference System and a Spatial City Model," in IEEE Transactions on Vehicular Technology, vol. 69, no. 5, pp. 4842-4854, May 2020, doi: 10.1109/TVT.2020.2983220.
Raja, K. B., & Nirmal, M. (2015). Parkinson's Disease Classification from Brain MRI Images using Hybrid Fuzzy-Neuro Approach with PSO Algorithm. In Proceedings of the International Conference on Communication and Signal Processing (pp. 0654-0657). IEEE.
M. Xia and C. Shi, "Autonomous Pedestrian Altitude Estimation Inside a Multi-Story Building Assisted by Motion Recognition," in IEEE Access, vol. 8, pp. 104718-104727, 2020, doi: 10.1109/ACCESS.2020.3000313.
Kalpana, V. (2016). Classification of Parkinson's Disease from MRI Images using Hybrid Approach with PSO Algorithm. International Journal of Advanced Research in Computer and Communication Engineering, 5(1), 120-124.
Dhabliya, D. (2021b). Blockchain Technology and Its Growing Role in the Internet of Things. In Intelligent and Reliable Engineering Systems (pp. 156–159). CRC Press.
Ghosh, P., Paul, A., & Chatterjee, S. (2017). Diagnosis of Parkinson's Disease from Brain MRI Images using ANFIS Classifier with PSO Algorithm. In Proceedings of the International Conference on Advanced Computing and Intelligent Engineering (pp. 449-457). Springer.
H. -Y. Chen and C. -H. Lee, "Vibration Signals Analysis by Explainable Artificial Intelligence (XAI) Approach: Application on Bearing Faults Diagnosis," in IEEE Access, vol. 8, pp. 134246-134256, 2020, doi: 10.1109/ACCESS.2020.3006491.
Tripathy, R., & Kar, R. (2019). Automatic Classification of Parkinson's Disease using Fuzzy Classifier with Particle Swarm Optimization. Journal of Medical Systems, 43(1), 1-11.
Sowmiya, R., & Valarmathy, S. (2015). Automated Diagnosis of Parkinson's Disease using ANFIS Classifier. In Proceedings of the International Conference on Innovations in Information, Embedded and Communication Systems (pp. 1-5). IEEE.
Sultana, N., Islam, M. M., Ahmed, M. U., & Islam, M. R. (2018). A Comparative Study of Particle Swarm Optimization and Genetic Algorithm in ANFIS for Diagnosis of Parkinson's Disease. International Journal of Computer Science and Network Security, 18(4), 166-172.
Khatoon, N., Bhateja, V., & Pal, A. K. (2021). Diagnosis of Parkinson's Disease using Hybrid ANFIS-PSO Model with K-means Clustering Technique for Feature Extraction. Applied Soft Computing, 108, 107527.
Fu, J. ., & Saad, N. H. M. . (2023). Cross Border E-Commerce Uses Blockchain Technology to Solve Payment Risks. International Journal on Recent and Innovation Trends in Computing and Communication, 11(3s), 205–215. https://doi.org/10.17762/ijritcc.v11i3s.6182
Omondi, P., Rosenberg, D., Almeida, G., Soo-min, K., & Kato, Y. A Comparative Analysis of Deep Learning Models for Image Classification. Kuwait Journal of Machine Learning, 1(3). Retrieved from http://kuwaitjournals.com/index.php/kjml/article/view/128
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
All papers should be submitted electronically. All submitted manuscripts must be original work that is not under submission at another journal or under consideration for publication in another form, such as a monograph or chapter of a book. Authors of submitted papers are obligated not to submit their paper for publication elsewhere until an editorial decision is rendered on their submission. Further, authors of accepted papers are prohibited from publishing the results in other publications that appear before the paper is published in the Journal unless they receive approval for doing so from the Editor-In-Chief.
IJISAE open access articles are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. This license lets the audience to give appropriate credit, provide a link to the license, and indicate if changes were made and if they remix, transform, or build upon the material, they must distribute contributions under the same license as the original.