# Deep Belief Networks (DBN) with IoT-Based Alzheimer's Disease Detection and Classification
## 2 Literature Survey
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Author** & **Methods** & **Performance** & **Advantages** & **Year** \\ \hline [PERSON] et al. (2017) & Gaussian Mixture Model and Deep Belief Network model & Accuracy of 90.28\% and 86.76\% on the binary and multiple class classifications & Use Automatic Speech Recognition (ASR) for detection. & 2022 \\ \hline [PERSON] et al. (2019) & AlexNet and CNN & KNN- 57.32\% & It proposed a model that extract deep features and solve the issues of imbalance and overfitting & 2021 \\ \hline [PERSON] et al. (2016) & 3D CNN & SVM- 72\% & It minimizes the effect of missing extra and allows prediction. & 2021 \\ \hline [PERSON] et al. (2018) & ResNet-18, Transfer Learning (fine-tuning, 2D CNN, off-the-shelf) & off-the-shelf 97.92\% fine-tuning 97.88\% ResNet-18 97.37\% & MRI pictures with less noise and less non-brain tissue are produced, which improves learning accuracy. & 2020 \\ \hline [PERSON] et al. (2019) & Siamese CNN (VGG16 model with additional convolution layer in the framework) & Proposed Siamese CNN & Enhancing dataset contrast will enhance model performance. & 2020 \\ \hline [PERSON] et al. (2018) & VGG16 & Accuracy of 95.74\% in 3-way classification 99.15\% (AD vs. CN) 99.30\% (AD vs. MCI) & Avoid over-fitting problem & 2019 \\ \hline [PERSON] et al. (2018) & CNN & LeNet-5 98.79\% GoogLeNet 98.84\% from & They utilized a high pass filter having frequency of 0.01 H to eliminate low-level noise from photographs. & 2016 \\ \hline [PERSON] et al. (2018) & CNN (pre-trained AlexNet model) & The model with Data Aug. 98.41\% Model without Data Aug. 85.15\% & overcoming the problem of overfitting and raising the importance of testing accuracy & 2019 \\ \hline [PERSON] et al. (2018) & VGG16 model with additional convolution layer & Accuracy Achieved 99.05\% maximum features & Effective in understanding maximum features & 2018 \\ \hline [PERSON] et al. (2019) & CNN with transfer learning & Accuracy is achieved with 92.50 \% (AD vs. NC) 85.00\% (AD vs. MCI) 80.00\% (MCI vs. NC.) & increasing classification accuracy and learning performance & 2019 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Survey of existing literature on AD detection. [PERSON] et al. [37] pre-processed the images by resizing them to 255 in brightness and contrast. To train and categorize diseases, CNN models are used [38; 39]. The images were divided into three groups (AD, MCI, and NC) that used 9540 images in the model-training process. The CNN model consists of four ReLU activation layers, three max-pooling layers, and three convolutional layers. Various optimizers were implemented, including Adam, S.G.D., Adadelta, Nadam, Adagrad, and Rmsprop. Adagrad outperforms competing optimizers in the proposed framework by achieving higher precision at a lower cost. The proposed model performed at a 98.57 per cent accuracy on the ADNI dataset. [PERSON] et al. [40] have contributed to this field. It is suggested that deep learning models be developed to facilitate automated feature extraction from imaging data. Scientists are working to develop a deep-learning model for reliable disease diagnosis. Many medical imaging modalities, from CT and MRI scans to x-rays and ultrasounds, have benefited greatly from applying deep learning models [41]. The most recent proposal for a cutting-edge ML system to automatically and swiftly diagnose AD was made by [PERSON] et al. [42]. The binary classifier used for this study was trained with 196 subjects' volumetric MRI data. The training procedure benefited from this information. These data were utilized in a number of ways during the training process. [PERSON] et al. [43] demonstrated comparable performance. Both the residual network and the plain 3D CNN architectures showed very long depth and complex behavior when trained on 3D structural MRI. Their results were subpar in comparison to what was hoped for. In order to introduce a CNN architecture, [PERSON] et al.