Jhalak Gupta

A. H. Rashid, A. Gupta, J. Gupta et al.

Alzheimer's Disease (AD) is a neurodegenerative disease that is one of the significant causes of death in the elderly population. Many deep learning techniques have been proposed to diagnose AD using Magnetic Resonance Imaging (MRI) scans. Predicting AD using 2D slices extracted from 3D MRI scans is challenging as the inter-slice information gets lost. To this end, we propose a novel and lightweight framework termed 'Biceph-Net' for AD diagnosis using 2D MRI scans that model both the intra-slice and inter-slice information. Biceph-Net has been experimentally shown to perform similar to other Spatio-temporal neural networks while being computationally more efficient. Biceph-Net is also superior in performance compared to vanilla 2D convolutional neural networks (CNN) for AD diagnosis using 2D MRI slices. Biceph-Net also has an inbuilt neighbourhood-based model interpretation feature that can be exploited to understand the classification decision taken by the network. Biceph-Net experimentally achieves a test accuracy of 100% in the classification of Cognitively Normal (CN) vs AD, 98.16% for Mild Cognitive Impairment (MCI) vs AD, and 97.80% for CN vs MCI vs AD.

Ashutosh Anshul, Jhalak Gupta, Mohammad Zia Ur Rehman et al.

The worldwide impact of the recent COVID-19 pandemic has been substantial, necessitating the development of accurate forecasting models to predict the spread and course of a pandemic. Previous methods for outbreak forecasting have faced limitations by not utilizing multiple sources of input and yielding suboptimal performance due to the limited availability of data. In this study, we propose a novel approach to address the challenges of infectious disease forecasting. We introduce a Multilateral Attention-enhanced GRU model that leverages information from multiple sources, thus enabling a comprehensive analysis of factors influencing the spread of a pandemic. By incorporating attention mechanisms within a GRU framework, our model can effectively capture complex relationships and temporal dependencies in the data, leading to improved forecasting performance. Further, we have curated a well-structured multi-source dataset for the recent COVID-19 pandemic that the research community can utilize as a great resource to conduct experiments and analysis on time-series forecasting. We evaluated the proposed model on our COVID-19 dataset and reported the output in terms of RMSE and MAE. The experimental results provide evidence that our proposed model surpasses existing techniques in terms of performance. We also performed performance gain and qualitative analysis on our dataset to evaluate the impact of the attention mechanism and show that the proposed model closely follows the trajectory of the pandemic.