Abstract
Alzheimer’s disease (AZD) is considered as the most common neurodegenerative disease that generally affects people older than 65 years. It causes irreversible brain commotion that belatedly partitions the memory and thinking abilities on progressively collapsing the capability of accomplishing daily activities. Early detection of AZD symptoms slows down its progression to the next stage and eventually protects the patient from severity of the disease. It is evidenced from various research studies that, application of nature-inspired algorithms in the context of image processing, have depicted decent results in analyzing complex image datasets related to the medical domain. This paper presents a modified BAT optimization algorithm integrated with a novel histogram model to extract ROI (Region of Interest) from brain scan MRIs (Magnetic Resonance Images) to automatically detect and predict the progression of AZD. The experimental analysis presented in the paper indicates that the proposed modified BAT algorithm outperforms state-of-art optimization algorithms.
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Nasina, S.S., Reddy, A.R.M. (2021). A Modified BAT Optimization Algorithm to Segment MRIs of Brain Subregions for Early Detection of Alzheimer’s Disease. In: Jyothi, S., Mamatha, D.M., Zhang, YD., Raju, K.S. (eds) Proceedings of the 2nd International Conference on Computational and Bio Engineering . Lecture Notes in Networks and Systems, vol 215. Springer, Singapore. https://doi.org/10.1007/978-981-16-1941-0_32
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DOI: https://doi.org/10.1007/978-981-16-1941-0_32
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