## 1 Introduction  
Alzheimer's disease (AD), the most common form of dementia, poses a significant challenge to healthcare providers. It causes a progressive, irreversible brain disorder characterised by a deterioration of cognitive functions. In the United States, Alzheimer's disease is the fifth leading cause of death among adults age 65 and older, and there was a 145% increase in deaths from Alzheimer's disease in 2017 [1]. Recently, a significant amount of research [2, 3, 4, 5, 6] has been conducted in order to detect early and pre-symptomatic stages of AD to slow or prevent the progression of the disease. Surprisingly, simple cognitive assessments of dementia were only accepted by a minority of the older adults. In particular, techniques based on MRI testing have been developed and used to identify AD [5]. The accuracy of an AD diagnosis mostly depends on the biomarkers of the disease. The features to detect Alzheimer are extracted from the region of interest (ROI) of the brain which has the potential to contain the tissue that are sensitive markers for dementia. Similarly, the distinctive features are confirmed by the histopathology of dementia that display these ROI in the amygdala and hippocampus embedded deep into the temporal lobe of the brain [7, 8]. Due to the recent advancements in automating the prediction of early diagnosis of dementia, the objective has shifted to help non-experts and non-neurologist users to identify early signs of AD. AI-based computer-aided diagnosis (CAD) has become a feasible and a popular tool in medicine due to its low cost and transparent decision making [9; 10; 11; 12]. Traditional methods of feature extraction require handcrafted features [13; 14; 15; 16]. [PERSON] et al. [17] combined advanced qMRI measurements along with conventional machine learning techniques to classify AD from Vascular Dementia (VD) and investigated if the suggested method might be used to forecast the common illness in those who do not have distinct AD or VD profiles. In the work proposed by [PERSON] et al. [18], four machine learning algorithms were trained with three different sets of experiments--extracting features manually, extracting features automatically, and ensembling all the models. [PERSON] et al. [19] manually extracted brain characteristics using the histogram approach in order to feed the data into the Random Forest model. The proposed method is not only a conventional process, but it is also time-consuming and leads to accuracy that is subpar. On the other hand, deep learning (DL) approaches are incredibly effective since they automatically extract key characteristics from the data. Transfer learning may recognise new things in accordance with the knowledge that is learnt from other things, just like human cognition can. Based on the notion that transfer learning can efficiently use the knowledge of old domains to identify the goals of a new domain, numerous enhanced transfer learning methodologies have been presented in recent years. [PERSON] et al. [20] employed two strategies: one is ensembling different machine learning algorithms via two methods, gradient boosting and a voting classifier, and the second is training an artificial neural network (ANN) to detect AD. A lot of researchers have confined their studies of classification of AD to the ADNI dataset, as investigated in some papers [21; 22; 23]. However, ensembles of deep convolutional architectures and similar networks that use deep learning have produced the best results. [PERSON] et al. [21] presented an ensemble of 3D densely connected convolutional networks (3D-DenseNets) for AD and MCI diagnosis. The use of a probabilistic-based ensemble method showed an increase in accuracy. In this paper, we propose to use the ensemble technique by combining the predictive capabilities of various state-of-the-art pre-trained transfer learning models for AD diagnosis.