This project is aimed at developing machine learning algorithms for Glioma semantic segmentation, and for the assessment of the risk of tumor core on brain and cognition. Early stages of development includes the development of a deep convolutional neural network based encoder-decoder network for automated Glioma semantic segmentation. Model shows to be highly performance-efficient and achieves it at minimized computational cost. 

This project focuses on the development of machine learning algorithms for identifying and detecting digital biomarker with early signs of Alzheimer disease progression. The goal of the project is to create a tool to be used by neurologist in identifying early-on mild cognitive patients who are more likely to progress to Alzheimer disease and can be used to discover the pattern of Alzheimer disease progression and severity for patients who already have Alzheimer disease.  Progress has begun with development of CNN architecture for discriminating between mild cognitive impairment and Alzheimer disease in order to understand the relationship that exist between the disease states before further research is carried out towards progression. 

The aim is to develop artificial intelligence (AI) algorithms for seizure-to-body pulse associative learning to investigate the correspondence between an epileptic seizure and human pulses. This will aid in the development of a machine learning model for predicting seizure onset in order to improve the quality of life of people living with epilepsy. Then will eventually be used to design AI-controlled wearable non-invasive devices for monitoring and managing epipleptic seizure.