# Deep Belief Networks (DBN) with IoT-Based Alzheimer's Disease Detection and Classification
## 2 Literature Survey  
[PERSON] et al. [23] created a new optimization strategy to enhance the mixed-norm regularised formulation. When tested on the ADNI datasets, cognitive measurements using multimodality or single MRI modality data showed enhanced classification performance and a condensed set of AD biomarkers was produced. The use of full 3D image data for differential diagnosis may call for larger training sets. Artificial intelligence algorithms that have been trained on large datasets may be more useful than CAD when applied to medical images. Because of this, the current state of AI-based medical image analysis is limited. When presented with novel images captured under extremely diverse conditions, artificial intelligence techniques quickly degrade from their stellar performance in a lab setting with uniform imaging protocols. [PERSON] et al. [24] compared three models to determine which one was the most accurate. The first model involves preparing an image for classification using SVM, KNN, or Random Forest by applying manually created features. The second model employs a convolutional neural network (CNN) deep learning model with cleaned and prepped data. AlexNet is used to extract deep features in SVM, k-nearest neighbor, and Random Forest. SVM classifiers performed best for the deep features-based model. K-nearest neighbor algorithms have an accuracy of 57.32 per cent, whereas SVM have an accuracy of 95.21 per cent, and random forest algorithms have an accuracy of 93.9 per cent. [PERSON] et al. [25] suggested a new deep learning architecture that combines 3D-enhanced CNN with stacked bidirectional RNN (SBi-RNN CNNs). Extraction of deep feature depiction from MRI and PET images is the focus of this paper, which argues for the use of a simple 3D-CNN framework. Using SBi-RNN enhanced the functionality of locally deep cascaded and compressed features. Several trials were conducted on the ADNI dataset to evaluate the recommended structure's efficacy. Analyzing the proposed architecture against the NC cohort revealed an average precision of 64.47 per cent for sMCI, 94.29% for AD, and 84.66% for pMCI. [PERSON] et al. (2016) combined data from various sources, including MRI scans, to better comprehend AD. Every CNN was trained with every type of data. Then, an integrated classification was carried out using random forests, SVMs, trees, and k-nearest neighbours. Evidence herein shows that combining data from multiple sources improves prediction accuracy. The small size of its dataset hinders the current study. The survey is presented in Table 1.  
To reveal the dementia and mild cognitive deficiency, [PERSON] et al. (2019) suggest a set of densely linked 3D convolutional networks (3D-DenseNets) using a probability-based fusion technique (MCI). In 3D-DenseNets, all of the layers are interconnected, which speeds up the gradient propagation. MRI scans are used as the training data. The results of DenseNets are also calculated with the help of a softmax function. The classification accuracy of DenseNets was 97.52 per cent when tested on the ADNI dataset. With the help of longitudinal data collected before the testing period and a random forest regression algorithm, [PERSON] et al. (2019) could predict the subjects' cognitive scores. Precise, constant, and medically instinctive models can be obtained by analyzing multi-modal time series data with the right ML models. The usage of multimodal time series data is likely to improve model performance when attempting to predict the progression of AD.