In this paper, we present a detailed study on deep learning approaches for the binary classification of Alzheimer’s disease stages using both unimodal and multimodal frameworks. Alzheimer’s disease is a progressive neurodegenerative brain disorder and the leading cause of dementia, characterized by cognitive decline, m...
Sraddha Sigdel, Stuti Khadka, Sujal Singh et al.· Journal of Hillside College...· 0 citations
This century has seen a substantial increase in the value of data. Many decisions are made based on data. More the data, more possibility to get accurate result. But in many cases, available datasets are imbalanced i.e. one class have more data and other class have few data which leads to inaccurate classification whil...
Ajaya Shrestha, Dhiraj Pyakurel, Amit Rauniyar· Journal of Hillside College...· 0 citations
In real world applications, data are generated with uneven distribution called imbalance data which consists of majority and minority classes. For imbalanced data most of the classifier is biased towards the majority class. This means the classifier provides good accuracy for the majority class but very poor accuracy f...
Amit Rauniyar, Prem Chandra Roy, Anisha Pokhrel et al.· Journal of Hillside College...· 0 citations
A systematic comparative evaluation of three deep learning-based segmentation architectures — U-Net, U-Net++, and Y-Net — for automated identification of DME and Intraretinal Fluid regions in OCT scans demonstrates the feasibility of deep learning-based OCT segmentation as a diagnostic support tool in resource-constrai...
Dhiraj Pyakurel, Yokisha Poudel, Sushiksha Prasai et al.· Journal of Hillside College...· 0 citations
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