A Novel Approach Using Deep Convolutional Neural Networks for Automated Dementia Detection and Classification

Abstract

Dementia, defined by gradual cognitive impairment affecting memory, thinking, and everyday functioning, offers considerable problems in early diagnosis and treatment. This paper offers a unique strategy leveraging deep convolutional neural networks (CNNs) for automated dementia identification and classification. CNNs specialize at learning detailed patterns from medical pictures, seeking to boost diagnostic accuracy and efficiency. The research relies on training a CNN architecture especially tuned for dementia classification utilizing a selected dataset of brain pictures. Transfer learning using a pre-trained CNN model fine-tunes it to distinguish minor neurological signals indicative of distinct dementia stages. The model undergoes training and validation on a varied dataset comprising classes such as Misdemeaned, Moderate Demented, Non-Demented, and Very Mild Demented, assuring strong performance across different degrees of dementia severity. In practical application, users may submit brain scan pictures via a web interface.