Classification and Analysis of HIV Neurocognitive MRI Images using Support Vector Machine
Abstract
Medical imaging has expanded thanks to advances in processing power and advanced image analysis techniques, especially with magnetic resonance imaging (MRI), which offers comprehensive body scans for diagnosis. This work proposes a simple yet efficient method to use a support vector machine (SVM) to classify HIV neurocognitive MRI pictures into normal and pathological categories. The model consists of four steps: data pre-processing, feature extraction, SVM classification, and model evaluation. To separate desired and undesired elements, such as the scalp and skull, pre-processed images were converted from grayscale to colour using support vector machines. The discrete wavelet transform (DWT) was used in the feature extraction stage to extract image properties. Colour moments (CMs) were then used to optimize the feature collection. Afterwards, the SVM classifier was used to determine the ideal feature set to classify images. For example, a dataset is used for training and testing, with a split ratio of 75% to 25% respectively. The experimental results show that the proposed model has a high classification accuracy of 94.4%
Downloads
References
M. H. Siddiqi, M. Azad, and Y. Alhwaiti, “An Enhanced Machine Learning Approach for Brain MRI Classification,” 2022.
“Retracted : A Precise Medical Imaging Approach for Brain MRI Image Classification Computational Intelligence and Neuroscience,” vol. 2022, p. 9836519, 2023.
M. Computing, R. Alrais, and N. Elfadil, “Support Vector Machine ( SVM ) for Medical Image Classification of Tumorous,” vol. 9, no. 6, pp. 37–45, 2020.
M. Ahmad, I. Shafi, and A. Osman, “Classification of Tumors in Human Brain MRI using Wavelet and Support Vector Machine,” vol. 8, no. 2, pp. 25–31, 2012.
F. Sheet, “FACT SHEET 2023 Global HIV sta � s � cs People living with HIV People living with HIV accessing an � retroviral therapy New HIV infec � ons AIDS- related deaths Key Popula � ons,” 2023.
J. Stover, P. Johnson, B. Zaba, M. Zwahlen, F. Dabis, and R. E. Ekpini, “The Spectrum projection package : improvements in estimating mortality , ART needs , PMTCT impact and uncertainty bounds,” vol. 84, no. Suppl I, pp. 24–30, 2008, doi: 10.1136/sti.2008.029868.
Y. Qi et al., “Early prediction of putamen imaging features in HIV-associated neurocognitive impairment syndrome,” pp. 1–10, 2021.
W. M. Microstructure et al., “Associations of Alcohol Use, HIV Infection, and Age with Brain White Matter Microstructure,” vol. 27, no. 6, pp. 936–950, 2022, doi: 10.1007/s13365-021-01021-8.Associations.
M. Y. Khachane, “Organ-Based Medical Image Classification Using Support Vector Machine,” vol. 8, no. 1, pp. 18–20, 2017, doi: 10.4018/IJSE.2017010102.
P. A. Pelizari, “International Journal of Applied Earth Observation and Geoinformation Semi-supervised learning with constrained virtual support vector machines for classification of remote sensing image data,” vol. 125, no. February, 2023, doi: 10.1016/j.jag.2023.103571.
M. Fayaz, N. Torokeldiev, S. Turdumamatov, M. S. Qureshi, M. B. Qureshi, and J. Gwak, “An Efficient Methodology for Brain MRI Classification Based on DWT and Convolutional Neural Network,” 2021.
S. W. Kareem, “Medical Image Classification Using Different Machine Learning Algorithms Sami H. Ismael,” vol. 1, no. 1, pp. 135–147, 2020.
B. Gaonkar and C. Davatzikos, “for SVM Based Image Classification and Group Comparisons,” pp. 723–730, 2012.
D. S. Mane and B. B. Gite, “Brain Tumor Segmentation Using Fuzzy C-Means and K-Means Clustering and Its Area Calculation and Disease Prediction Using Naive-Bayes Algorithm,” pp. 21342–21347, 2017, doi: 10.15680/IJIRSET.2017.0611052.
H. D. Cheng, J. Shan, W. Ju, Y. Guo, and L. Zhang, “Automated breast cancer detection and classification using ultrasound images : A survey,” Pattern Recognit., vol. 43, no. 1, pp. 299–317, 2010, doi: 10.1016/j.patcog.2009.05.012.
I. Wahlang et al., “Brain Magnetic Resonance Imaging Classification Using Deep Learning Architectures with Gender and Age,” pp. 1–20, 2022.
M. Senden, T. C. Emmerling, R. Van Hoof, M. A. Frost, and R. Goebel, “Reconstructing imagined letters from early visual cortex reveals tight topographic correspondence between visual mental imagery and perception,” Brain Struct. Funct., vol. 224, no. 3, pp. 1167–1183, 2019, doi: 10.1007/s00429-019-01828-6.
R. Jha, V. Bhattacharjee, and A. Mustafi, “Transfer Learning with Feature Extraction Modules for Improved Classifier Performance on Medical Image Data,” vol. 2022, 2022.
Z. Yu, Q. He, J. Yang, and M. Luo, “A Supervised ML Applied Classi fi cation Model for Brain Tumors MRI,” vol. 13, no. April, pp. 1–8, 2022, doi: 10.3389/fphar.2022.884495.
Chang,L. e. a. "SVM-Based Classification of HIV-Associated Neurocognitve Disorder," Jornal of Neuroimaging, 2019.
Smith,J. e. a. "Neurocognitive Impairment Detection in HIV Using Structural MRI," Jornal of NeuroVirology, 2021.
Kumar, S. e. a. "Functional MRI Analysis using SVM in HIV Patients," Neuroimaging, 2020.
Lee,A. e. a. "SVM Classifier for HIV-Related Brain Changes," Brain Imaging and Behavior , 2018.
Patel, R. e. a. "SVM-Based Neuroimaging in HIV: A Fuctional MRI study," Neuroinformatics, 2017.
Rivera, M. e. a. "Structural MRI and SVM for HIV Cognitive Disorder Classification," Jornal of Magnetic Resonaces Imaging, 2022.
Y. e. a. Zhang, "Advanced Pre-processing Techniques in Neurocognitive Disorder Classification," Jornal of Machine Learning in Healthcare, 2023.
D. e. a. Clifford, "Challenges in HIV-Related Neurocognitive Disorder Research," AIDS Research and Therapy, 2022.
Copyright (c) 2024 Richard P. Mwanjalila, Charles Okanda Nyatega, Cuthbert John Karawa, Joseph Sospeter Salawa, Elizabeth Odrick Koola, Phocas Sebastian
This work is licensed under a Creative Commons Attribution 4.0 International License.
