Abstract
Deep learning (DL) approaches have gained significant traction in recent years for medical image analysis. In this research, magnetic resonance imaging (MRI) data were employed to classify Alzheimer’s disease (AD) using DL techniques. Alzheimer’s disease (AD), a fatal neurological condition, is characterized by early signs such as memory loss and cognitive decline. Accurate and timely diagnosis is crucial for appropriate patient treatment and subsequent care. Unlike traditional feature learning methods like machine learning, deep learning approaches can directly learn complex and high-level features from datasets. However, this research extends the classification procedure by incorporating forecasting for additional disease stages. The proposed approach obtained an impressive accuracy of 98.02% using the XCeption model. The automatic extraction of intricate features from MRI data empowers improved diagnostics, proactive disease management, and personalized treatment strategies for individuals affected by Alzheimer's disease.
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Sajid Anam Ifti, M., Redwan Ahmed, M., Arafat Rahman, S.M., Afridi, S.S., Jennifer, S.S., Reza, A.W. (2023). Innovative Method for Alzheimer’s Disease Detection Using Convolutional Neural Networks. In: Shakya, S., Tavares, J.M.R.S., Fernández-Caballero, A., Papakostas, G. (eds) Fourth International Conference on Image Processing and Capsule Networks. ICIPCN 2023. Lecture Notes in Networks and Systems, vol 798. Springer, Singapore. https://doi.org/10.1007/978-981-99-7093-3_16
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DOI: https://doi.org/10.1007/978-981-99-7093-3_16
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