Detection Of Alzheimer’s Disease Using ELRFXG Booster And ELCR Machine Learning Algorithms

Abstract

Image processing and analysis techniques have become more prevalent within the medical field as they aid in the detection, diagnosis and prognosis of conditions that threaten people’s lives. Some conditions, however, present additional challenges to medical experts, requiring a further and more advanced examination of the patient’s data before a decision can be made. There are many health issues related to brain, Alzheimer disease is one among them. Alzheimer’s disease is a chronic condition that leads to degeneration of brain cells leading at memory enervation. Alzheimer’s Disease (AD) is the most common dementia affecting cognitive domains such as memory and learning, perceptual-motion or executive function. This Research work uses various steps involved in the image processing to analysis the Alzheimer affected parts of brain MRI. The pre-processing, Image segmentation, Feature extraction and analysing Alzheimer disease are the basic steps followed during the research work. The implementation part of the research work consists of introducing Deep learning concept to find the Alzheimer effected parts of the brain MRI. The proposed ELRFXG and ELCR are useful in finding the Alzheimer effected parts within a fraction of seconds. The proposed model prediction process is accurate compared with the other classification algorithms. The proposed model is compared with other Machine Learning algorithms to test the performance. Machine Learning algorithms such as CNN, RNN, TL, RF, XG-Boost were used for comparison with proposed ELRFXG and ELCR algorithm. The measurement and testing strategy followed for each sample are common for showing transparency in simulation results and testing strategy. The identification process followed in the research work can lead to take preventive measures before the situation becomes worst for the patient.