Deep CNN-based Classification of Brain MRI Images for Alzheimer’s Disease Diagnosis
Journal Title: International Journal of Experimental Research and Review - Year 2024, Vol 41, Issue 5
Abstract
As the leading cause of dementia worldwide, Alzheimer's disease afflicts millions, with progressively impaired abilities to carry out daily activities or communicate and even recognize faces. Although the cause behind lupus is not fully understood, it probably reflects lifestyle choices and environmental factors as well as genetic propensity. The largest obstacles in the diagnosis of these diseases are their often subtle early manifestations and absence of sensitive detection paradigms. Deep-learning algorithms first came to the forefront of medical imaging just a few years ago and were celebrated as sophisticated diagnostic aids, able to spot subtle signs in scans usually hidden from human eyes. We are benefitting from the use of these state-of-the-art algorithms to improve Alzheimer's detection, with one of the largest MRI datasets available today (more than 86,000 images) being used to train our model. In view of this vast data set, it was appreciably combined one to be accurate-centric diagnostic tool. The performance of our novel deep learning model is strong and provided state-of-the-art validation accuracy (99.63%), surpassing existing models These figures highlight the great promise of our model as a verifiable method for detecting early-stage Alzheimer's disease - a significant concern in controlling and managing disease progression. Our research truly is a major step forward in the field of Alzheimer's disease diagnosis by employing cutting-edge deep learning techniques. Early diagnosis allows for better treatment and lower disease burden that can prevent morbidity, mortality and even change many patient outcomes. This is a considerable improvement toward diagnosing Alzheimer's disease with the help of artificial intelligence and presents an expectation for more exact and timely finding.
Authors and Affiliations
D. Dakshayani Himabindu, B. Pranalini, Mirtipati Satish Kumar, Alluri Neethika, Bhavya Sree N, Manasa C, Harshitha B, Keerthana S
Keywords
Related Articles
- EP ID EP741627
- DOI 10.52756/ijerr.2024.v41spl.004
- Views 47
- Downloads 0
Precision fault prediction in motor bearings with feature selection and deep learning
In the disciplines of industrial machinery, mechanical engineering is beneficial to recognize motor performance for motors with HP power, torque transducer, dynamometer, and control electronics. The motivation is to addr...
AGWO: Cost Aware Task Scheduling in Cloud Fog Environment Using Hybrid Metaheuristic Algorithm
In IoT concepts, efficient approaches like cloud-fog computing are emerging, enhancing system benefits. The performance and output of such frameworks can be greatly improved by optimized scheduling of Internet of Things...
Endocrine Disruptor—A threat to the animal world
Various types of naturally occurring and artificially made chemicals cause disruption of endocrine processes among animals. They mimic biochemically with hormones and interfere with the normal signaling and activity of t...
A comparative physico-chemical, phytochemical and spectroscopic analysis of two medicinal plants belongs to Euphorbiaceae family: Acalypha indica L. and Euphorbia hirta L. growing in Paschim Medinipur District, West Bengal, India
Plants belong to Euphorbiaceae family, bearing enormous Medicinal potential due to the presence of various pharmacologically important secondary metabolites. Now a days, with the help of various tools and techniques like...
Arsenic Uptake, Transport, Accumulation in Rice and Prospective Abatement Strategies - A Review
Recent reports claim that arsenic (As) toxicity affects millions of individuals worldwide. A significant problem for rice output and quality as well as for human health is the high content of arsenic (As), a non-essentia...