Deep Learning Algorithms for Early Alzheimer's Diagnosis: From Data to Prognosis

Alzheimer's disease (AD) is a neurological condition that worsens over time and affects millions of older people worldwide. An early diagnosis and correct classification of AD are essential for improving patient condition, enabling prompt intervention, and allocating healthcare resources. Healthcare professionals can use the system's predictions and classifications to make judgements about treatment regimens, patient counselling, and resource allocation. Additionally, by offering insightful information on the course of disease, potential therapeutic targets, and creation of fresh remedies, the system advances research into AD. To build reliable models for the precise diagnosis and classification of the disease, deep learning techniques were used to offer a cutting-edge AD prediction and classification system. To train and optimize deep neural networks, the system uses an AD dataset that consists of Magnetic resonance imaging(MRI) images. The dataset consists of 4 classes, Mild Demented, Moderate Demented, Non-Demented and Very Mild Demented. The proposed approach seeks to improve the precision, sensitivity, and specificity of AD prediction and classification by utilizing the power of deep learning. To identify complex patterns and correlations in the data, different deep learning architectures such as ResNet, MobileNet, and Xception are considered. ResNet exhibits the highest predictive accuracy among the three models, achieving an impressive 93%, followed closely by MobileNet at 91%, while Xception achieves an accuracy of 88%. The system's predictive models and classification algorithms can distinguish between various disease phases of the disease.