English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

Alzheimer's Disease (AD) is the most common type of dementia. In all leadingcountries, it is one of the primary reasons of death in senior citizens.Currently, it is diagnosed by calculating the MSME score and by the manualstudy of MRI Scan. Also, different machine learning methods are utilized forautomatic diagnosis but existing has some limitations in terms of accuracy. Inthis paper, we have proposed some novel preprocessing techniques that havesignificantly increased the accuracy and at the same time decreased thetraining time of various classification algorithms. First, we have convertedthe ADNI dataset which was in 4D format into 2D form. We have also mitigatedthe computation costs by reducing the parameters of the input dataset whilepreserving important and relevant data. We have achieved this by usingdifferent preprocessing steps like grayscale image conversion, Histogramequalization and selective clipping of dataset. We observed a highest accuracyof 97.52% and a sensitivity of 97.6% in our testing dataset.

Enhancing Learnability of classification algorithms using simple data preprocessing in fMRI scans of Alzheimer's disease