AlzheimerNet-V3: Automated Deep Learning Approach for Detecting Alzheimer's Disease

Inzamam Abid Chughtai ()
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Inzamam Abid Chughtai: UET Taxila, Punjab, Pakistan

International Journal of Innovations in Science & Technology, 2024, vol. 6, issue 1, 320-332

Abstract: Introduction/Importance of Study: Alzheimer’s Disease (AD) stands as the highly prevalent form of dementia, culminating in a progressive neurological brain disorder characterized by deteriorating memory function and impaired daily activities due to brain cell damage. This singular ailment is both unique and fatal, underscoring the critical importance of early detection worldwide. Timely identification holds promise in preemptively addressing the future challenges faced by numerous individuals.Novelty statement:By scrutinizing the disease's ramifications via MRI imagery, Artificial Intelligence (AI) technology emerges as a valuable ally in categorizing AD patients, thus aiding in prognosticating the onset of this debilitating illness. In recent years, AIfrom Machine Learning (ML) tactics have proven instrumental in the diagnostic landscape of AD. This study employs a transfer learning methodology to accurately identify Alzheimer's patients using MRI examination. Specifically, weintroduce anadapted deep learningmodel dubbed AlzheimerNet-V3, leveraging a tailored version of the Inception v3 architecture.Material and Method:Our investigation encompasses comprehensive experimentation and assesses the efficiency of AlzheimerNet-V3 in collaboration with further pre-trained specimens. Notably, AlzheimerNet-V3 achieves the conclusion of accuracy, the outcome of precision, recall, and development of F1-score was computed as94.06%for all traits. Furthermore, comparative analysis against contemporary techniques underscores the efficacy of AlzheimerNet-V3 for Alzheimer's detection, highlighting its reliability for real-time implementation.

Keywords: Early Detection; Dementia; Cognitive Decline; Diagnosis; Classification; AlzheimerNet-V3 (search for similar items in EconPapers)
Date: 2024
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