ARCHIVES
Case Study
Survey on Optimization Techniques Medical Image Feature Extraction for Brain Disease Prediction
Ravi V1
S Praveen Kumar Swamy2
Shreekanta K A3
1 2 3 Department of Computer Science Engineering in Data Science, Dayananda Sagar Academy of Technology and Management, Bengaluru, Karnataka, India.
Published Online: September-December 2025
Pages: 57-61
Cite this article
↗ https://www.doi.org/10.59256/indjcst.20250403011
References
1. Li, Q., Yang, X., Xu, J., Guo, Y., He, X., Hu, H., Lyu, T., Marra, D., Miller, A., Smith, G., DeKosky, S., Boyce, R. D., Schliep, K., Shenkman, E., Maraganore, D., Wu, Y., & Bian, J. (2023). Early prediction of Alzheimer’s disease and related dementias using real-world electronic health records. Alzheimer’s & Dementia, 19(8), 3506– 3518. https://doi.org/10.1002/alz.12967
2. Dara, O. A., Lopez-Guede, J. M., Raheem, H. I., Rahebi, J., Zulueta, E., & FernandezGamiz, U. (2023). Alzheimer’s disease diagnosis using machine learning: A survey. Applied Sciences, 13(14), 8298. https://doi.org/10.3390/app13148298
3. M. S. Chong and S. Sahadevan, “Preclinical Alzheimer’s disease: Diagnosis and prediction of progression,” The Lancet Neurology, vol. 4, no. 9, pp. 576–579, 2005, doi: 10.1016/S1474-4422(05)70168-X.
4. Malik, A. Iqbal, Y. H. Gu, and M. A. Alantari, “Deep learning for Alzheimer’s disease prediction: A comprehensive review,” Diagnostics, vol. 14, no. 12, p. 1281, 2024, doi: 10.3390/diagnostics14121281.
5. S. Palmqvist et al., “Blood biomarkers to detect Alzheimer disease in primary care and secondary care,” JAMA, vol. 332, no. 15, pp. 1245–1257, 2024, doi: 10.1001/jama.2024.13855.
6. M. V. Fernandez et al., “Genetic and multiomic resources for Alzheimer disease and related dementia from the Knight Alzheimer Disease Research Center,” Scientific Data, vol. 11,p. 768, 2024, doi: 10.1038/s41597-024-03485-9.
7. V. Patil, M. Madgi, and A. Kiran, “Early prediction of Alzheimer’s disease using convolutional neural network: A review,” Egypt. J. Neurol. Psychiatry Neurosurg., vol. 58, Art. no. 130, 2022, doi: 10.1186/s41983-022-00571-w.
8. K. H. Aqil, P. Dumpuri, K. Ram, and M. Sivaprakasam, “Predictive modeling of Alzheimer’s disease progression: Integrating temporal clinical factors and outcomes in time series forecasting,” Intelligence-Based Medicine, vol. 10, p. 100159, 2024, doi: 10.1016/j.ibmed.2024.100159.
2. Dara, O. A., Lopez-Guede, J. M., Raheem, H. I., Rahebi, J., Zulueta, E., & FernandezGamiz, U. (2023). Alzheimer’s disease diagnosis using machine learning: A survey. Applied Sciences, 13(14), 8298. https://doi.org/10.3390/app13148298
3. M. S. Chong and S. Sahadevan, “Preclinical Alzheimer’s disease: Diagnosis and prediction of progression,” The Lancet Neurology, vol. 4, no. 9, pp. 576–579, 2005, doi: 10.1016/S1474-4422(05)70168-X.
4. Malik, A. Iqbal, Y. H. Gu, and M. A. Alantari, “Deep learning for Alzheimer’s disease prediction: A comprehensive review,” Diagnostics, vol. 14, no. 12, p. 1281, 2024, doi: 10.3390/diagnostics14121281.
5. S. Palmqvist et al., “Blood biomarkers to detect Alzheimer disease in primary care and secondary care,” JAMA, vol. 332, no. 15, pp. 1245–1257, 2024, doi: 10.1001/jama.2024.13855.
6. M. V. Fernandez et al., “Genetic and multiomic resources for Alzheimer disease and related dementia from the Knight Alzheimer Disease Research Center,” Scientific Data, vol. 11,p. 768, 2024, doi: 10.1038/s41597-024-03485-9.
7. V. Patil, M. Madgi, and A. Kiran, “Early prediction of Alzheimer’s disease using convolutional neural network: A review,” Egypt. J. Neurol. Psychiatry Neurosurg., vol. 58, Art. no. 130, 2022, doi: 10.1186/s41983-022-00571-w.
8. K. H. Aqil, P. Dumpuri, K. Ram, and M. Sivaprakasam, “Predictive modeling of Alzheimer’s disease progression: Integrating temporal clinical factors and outcomes in time series forecasting,” Intelligence-Based Medicine, vol. 10, p. 100159, 2024, doi: 10.1016/j.ibmed.2024.100159.
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