AI Assisted Caretaker For Neurodegenerative Disorder

Abstract

Neurological disorders encompass a broad spectrum of diseases affecting the brain, spinal cord, and nervous system. Almost 600 neurological disorders exists globally. Early diagnosis is critical for mitigating the societal and economic impact of NeuroDegenerative Disorders (NDD). Alzheimer’s Disease (AD) is one of the most prevelent neurodegenerative disorder. Additionaly, there is no cure to AD. The early and precise diagnosis of AD remains a significant challenge in clinical neuro-science. Neuroimaging is one of the significant biomarker for early diagnosis of AD. The research work compraises of Magnetic Resonance Imaging (MRI) data acquired from Kaggle. Dataset consists of 4 classes: Mild Demented (8960), VeryMildDemented (8960), Moderate Demented (6464), NonDemended (9600) , 33706 samples in total. Pillow (PIL) is used for data preprocessing to greyscale, resize and normalize the image samples. For classification Convolutional Neural Network (CNN) is used with 3 convolutional blocks and 1 hidden layer. Grid Search is used for hyper-parameter tuning. Accuracy achieved is 97%. RAG with HuggingFace Transformer model embeddings is used to generate context-aware and intelligent responses. Notification system to notify patients and doctors about important events. Appointment Scheduling module is used to schedule and keep track of appointments between doctor and patients. Prescription table is used in system to keep record of patient prescription after an appointment.