© 2022 Ragavamsi Davuluri and Ragupathy Rengaswamy. This open-access article is distributed under a Creative Commons Attribution (CC-BY) 4.0 license. Journal of Computer Science Journal of Computer Science Original Research Paper Improved Classification Model using CNN for Detection of Alzheimer’s Disease Ragavamsi Davuluri and Ragupathy Rengaswamy Department of Computer Science and Engineering, Annamalai University, Chidambaram, Tamil Nadu, India Article history Received: 26-03-2022 Revised: 19-04-2022 Accepted: 07-05-2022 Corresponding Author: Ragavamsi Davuluri Department of Computer Science and Engineering, Annamalai University, Chidambaram, Tamil Nadu, India E-mail: raaga.vamsi@gmail.com Abstract: Alzheimer’s Disease (AD) is commonly called a neurodegenerative disorder and it is a common form of dementia. There is no permanent cure for this brain disease hence the early diagnosis of such disease using medical imaging system is highly significant. Machine learning models play a vital role in the detection of AD. Since most of the conventional machine learning models find it difficult to detect the essential features to classify the disease, an advanced deep learning framework called Convolutional Neural Network (CNN) is used in this study to detect essential features automatically and classify the disease. The building components of the proposed CNN-based classification method include convolution layer, batch normalization process, ReLU, and Max- pooling operation. The main objective of this CNN-based classification method is to predict whether the patient is suffering from Alzheimer's disease through the analysis of brain MRI. The proposed methodology implemented is identical to a classification-based system that undergoes training, evaluation, and testing process. Finally, the softmax layer is applied for classification, and the Adam optimization technique is applied for reducing the loss, and by applying Adam quicker convergence can be achieved. The proposed improved CNN classification method achieves an accuracy of 97.8%. Keywords: Alzheimer’s Disease Detection, Magnetic Resonance Imaging, Convolution Neural Network, Deep Learning Introduction Alzheimer's disease is a neuropsychiatric ailment that causes memory loss in persons over the age of 65. Alzheimer's disease affects 50 million people worldwide, with the number expected to nearly quadruple by 2050.The aberrant build-up of proteins in and around brain cells is assumed to be the origin of AD. The brain tissues get damaged due to Alzheimer’s disease and it may lead to nerve cell death if it was not diagnosed in its earlier stages. AD leads to memory loss and also disrupts the human body's functions like speaking, writing, and reading. Alzheimer's disease patients usually suffer from lung dysfunction, malnutrition, cognitive impairment (Soysal et al., 2014), and functional dependence. Precise and accurate detection of AD is quietly not possible since improper medications are required. Detection of AD in its earlier stages (also called a pre-clinical stage) can save the patient's life and also helps in the retreatment process (Dubois et al., 2016). The symptoms of AD will develop slowly but the defection effects are severe when it starts in the human brain (Picón et al., 2019). Several medical tests are required to detect Alzheimer's and it leads to the generation of multivariate heterogeneous data (Davuluri and Rengaswamy, 2020). A standard medical workup for Alzheimer's disease often includes structural imaging with Magnetic Resonance Imaging (MRI) or Computed Tomography (CT) (Davuluri and Rengaswamy, 2022a). To resolve various kind of problems that is related to brain image data analysis can be done by using Machine Learning (ML) and deep CNN based approaches (Mehmood et al., 2020). For diagnosing the disease, MRI is used (Davuluri and Rengaswamy, 2022b). Deep Learning (DL) algorithms are mostly applied for object recognition tasks and competitions like Imagenet Large Scale Visual Recognition Challenge (ILSVRC) (Russakovsky et al., 2015) that intersect with time and increased use of medical records and image detection process. DL algorithms are mostly used to analyze large datasets and it is very useful for the diagnosis of AD. CNN is an effective technique to detect and classify Alzheimer's and its stages using brain MRI images (Salehi et al., 2020).