## 1 Introduction
The proposed model was shown to be superior to five other state-of-the-art studies. Ref. [27] proposed a cascaded 3D-CNN for AD diagnosis using structural MR images. To classify the input, the CNN model first retrieved features from it. In order to diagnose Alzheimer's disease using 3D-MR brain pictures, the authors in [28] modified V-Net to partition the bilateral hippocampus. They demonstrate the need for accurate hippocampal segmentation for an accurate AD diagnosis model. Compared to other segmentation and classification approaches, they state that the proposed design performed better. [PERSON]. [29] developed a CNN model for AD diagnosis using MR images. To put the method to the test in real-world settings, the researchers looked at the correlation between relevance score and hippocampus volume. The 3D-CNN-SVM model for AD diagnosis was proposed in [30] based on MR images. It combines the 3D-CNN model to obtain features from MR images and SVM to classify the features. The 3D-CNN-SVM model provides a significant improvement over both 2D-CNN and classic 3D-CNN. Ref. [31] created a CNN using the DenseNet Bottleneck-Compressed architecture for the diagnosis of AD using MR images. The proposed model correctly classified the input 86% of the time. The EfficientNet models [32] are developed through the implementation of uncomplicated and exceptionally effective compound scaling methods. EfficientNet models demonstrate an enhanced level of precision and effectiveness in comparison to modern CNNs, including MobileNetV2, AlexNet, ImageNet, and GoogleNet [33]. EfficientNets show better accuracy through their compactness, computational efficiency, and generalization capabilities. Comparing eight different convolutional neural networks for early detection of Alzheimer's disease, the EfficientNetB0 model has better evaluation metrics and needs fewer model parameters [34]. In a recent study [35], an EfficientNetB0 model was employed to diagnose AD. The results obtained for all performance metrics varied from 87% to 95%. The EfficientNetB0 model is very good at finding COVID-19 patterns in X-ray images while using a small amount of computing power compared to other popular architectures like ResNets and VGGs [36]. Subsequently, EfficientNets demonstrated high efficiency in numerous applications, including the detection of malaria parasites from blood smears and various COVID-19 detection applications [37]. Thus, the results of the EfficientNet series on differ ent medical applications inspired us to develop an approach based on the EfficientNetB0 structure for AD. This study introduces a comprehensive methodology for evaluating the severity and progression of Alzheimer's disease (AD) from start to finish. Deep-learning techniques were utilized to distinguish between four stages of Alzheimer's disease, specifically normal control, very mild, mild, and moderate dementia. This research endeavor aims to improve the performance of efficientNetB0 by implementing a three-step data processing approach. These steps involve the use of an alpha-trimmed filter for low-pass filtering, histogram equalization, and the application of transfer-learning techniques. In order to evaluate our approach performance, we compared it to two commonly utilized models, specifically ResNet50 and VGG16. The evaluation metrics employed for comparison encompassed precision, recall, accuracy, F1 score, confusion matrices, and receiver operating characteristic (ROC). In the following sections, the materials and methods are described. The results from experimental data, using datasets from official GitHub and Kaggle repositories, of three DL models are compared using six performance metrics. The proposed method was compared with two other well-performing deep-learning algorithms. Then, the results were discussed, conclusions drawn, and future directives were suggested.