• N.Deepthi
• Dr.P.Sruthi
• Vijetha Rudra
• K.Bhavya Swarupa
• V.Harika Reddy

Alzheimer`s disease postures a noteworthy challenge in healthcare, requiring precise and early discovery for viable mediation and administration. In order to improve the model's capacity to identify patterns suggestive of AD, the suggested CNN architecture with sequential layers is intended to efficiently capture spatial dependencies within MRI Imaging data. Based on experimental results, we show that our CNN model with sequential architecture and SMOTE dataset augmentation performs better in AD detection than other approaches .Overall, our research demonstrates the effectiveness of using sophisticated deep learning strategies, like CNNs with sequential architecture and dataset augmentation techniques like SMOTE, to improve the precision and dependability of Alzheimer's disease detection systems, opening the door to better patient care and clinical outcomes.

Alzheimer's Disease, MRI Imaging, Convolutional Neural Networks (CNNs),SMOTE

"Alzheimer Disease Detection using CNN", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 4, page no.d692-d697, April-2024, Available :http://www.jetir.org/papers/JETIR2404390.pdf

"Alzheimer Disease Detection using CNN", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 4, page no. ppd692-d697, April-2024, Available at : http://www.jetir.org/papers/JETIR2404390.pdf