Paper Title :Deep Learning Leading to Revolution in Alzheimer’s Disease Detection

Author :Mohd Arif, Preet Pal, Mohit Kumar, Vinod Kumar

Article Citation :Mohd Arif ,Preet Pal ,Mohit Kumar ,Vinod Kumar , (2023 ) " Deep Learning Leading to Revolution in Alzheimer’s Disease Detection " , International Journal of Advance Computational Engineering and Networking (IJACEN) , pp. 63-68, Volume-11,Issue-6

Abstract : This paper discusses research on deep learning-based early Alzheimer's disease detection. Millions of individuals worldwide suffer with Alzheimer's disease, a neurodegenerative condition for which early identification is crucial for successful treatment. The strategy makes use of deep learning models to examine brain MRI pictures and categorise them as being indicative of Alzheimer's disease[1] or of a healthy brain. The study uses a publicly available dataset containing MRI scans of 416 subjects, of which 200 were healthy and 216 had Alzheimer's disease. Convolutional neural networks (CNNs) and recurrent neural networks (RNNs), among other deep learning models, were trained, tested, and their performance was assessed using a range of metrics. Our findings demonstrate that the proposed strategy performs better than current techniques for diagnosing Alzheimer's disease..This study contributes to the development of a reliable and efficient tool for Alzheimer's disease detection, which could improve early diagnosis and treatment of this devastating disease. Keywords - Alzheimer's Disease, Deep Learning, Magnetic Resonance Imaging (MRI), Convolutional Neural Networks (Cnns), Recurrent Neural Networks (Rnns) , Early Detection, Feature Extraction, Hyperparameter Tuning , Medical Ethics, Clinical Implications.

Type : Research paper

Published : Volume-11,Issue-6

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