Deep Learning-Based Alzheimer’s Risk Classification Using Inception V3 And Mri Data

Abstract

Alzheimer's disease is a progressive neurological illness that impairs memory, thinking, and day-to-day functioning. It has a significant negative influence on people and is becoming a bigger problem for healthcare systems worldwide. Determining Alzheimer's risk levels accurately and promptly is essential for starting early therapies that can decrease the disease's course and enhance quality of life. Conventional diagnostic techniques, such as clinical assessments and manual brain imaging analysis, are often laborious, arbitrary, and vulnerable to variation.In this work, we suggest an automated approach that uses a state-of-the-art convolutional neural network and the Inception V3 algorithm to categorize Alzheimer's risk into four groups: Moderately Demented, Very Mild Demented, Mild Demented, and Non Demented. To ensure strong model performance, the system is trained using a dataset of 6,400 MRI images that have been preprocessed to improve features and reduce noise. The Inception V3 model is refined to capture minor patterns in brain areas linked to various stages of Alzheimer's disease via transfer learning methods.The suggested model outperforms several current deep learning frameworks and conventional diagnostic techniques with an outstanding 98.6% accuracy rate in risk-level categorization. This technology greatly improves dependability and reduces diagnostic delays by automating the interpretation of complicated medical imaging data, assisting medical practitioners in making well-informed judgments. This work demonstrates the revolutionary potential of deep learning in Alzheimer's disease risk assessment and early detection, opening the door for scalable, reasonably priced diagnostic tools in clinical practice.
Index Terms: Alzheimer’s disease, Neurodegenerative Disorder, Medical Imaging, Early Detection, Inception V3 algorithm , Medical Imaging, Early Detection, Diagnostic Automation.