Abstract
Deep training, modern machine learning technology, and classic machine learning surpassed complex arrogant data, especially computer vision. The use of deep learning in the early diagnosis of Alzheimer's bottle (AD) has caused great attention because it has caused a large number of complex nerve image data due to the recent achievement in the neuro video method. Alzheimer is a kind of dementia. This brain disease affects people over 60 years old, but now it also affects middle -aged people. Therefore, we focus on this disease and try to control using various approaches. The removal of the function is a problem in predicting using the enormous processing of the data set, but the difficult thing is that it cannot find and extract reliable features in the data set. To solve this problem, we have introduced the SuperShip Neural Network (CNN), which is used for effective classification and extraction of signs. Removing and choosing a sign is an important aspect in the classification. To increase the accuracy and performance of the classification, we study the extraction and selection of functions. As a result, it is easier to get trusted results.
Keywords
Cognitive Assessments, Image Classification, Image Segmentation
Citations
IRE Journals:
Yogaselvan S , Vinoth M , Yuvan Sankar S
"Early Detection of Alzheimer Disease Detection" Iconic Research And Engineering Journals Volume 8 Issue 10 2025 Page 130-135
IEEE:
Yogaselvan S , Vinoth M , Yuvan Sankar S
"Early Detection of Alzheimer Disease Detection" Iconic Research And Engineering Journals, 8(10)
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IRE Journals is an open access online journals established with an aim to publish high quality of research work in various diciplines. We have more than 6 years in publications. Over 2500+ papers already published.
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