# A Review of Deep Learning Algorithms and Their Applications in Healthcare
## 4 A Comparison of Deep Learning Algorithms  
Table 1 highlights the main information and is arranged in an emphasized manner to summarize the representation of the deep learning algorithms that have been presented in this article, including their advantages, disadvantages, and applications, and whether the algorithm is supervised or unsupervised. Figure 27: BERT-Fine Tuning [69]. As listed in Table 2, Backpropagation is a simple supervised algorithm which is easy and fast to program. However, it needs excessive time for training and is very sensitive to noisy data. It can also be used for speech recognition, face recognition, character recognition, etc. An autoencoder is an unsupervised learning algorithm which is an efficient algorithm for learning several layers--mainly used for dimensionality reduction. However, it is a lossy technique. A variational autoencoder is an autoencoder that forms a high dimensional dataset into a distribution. The implementation of a variational autoencoder is more challenging than the implementation of an autoencoder. However, they produce high quality images. The main application of variational autoencoder is to generate new data related to the source data. RBMs are unsupervised learning algorithms useful for dimensionality reduction, classification, regression, feature learning, etc. They are restricted in terms of the connections between the input layers and the hidden layers. They are also very tricky to train, and the loss cannot be tracked. DBNs are also unsupervised learning algorithms belonging to a generative graphical model. They identify deep patterns in the input data. DBNs can learn an optimal set of parameters quickly even for models with large numbers of parameters and layers with nonlinearity, but they are very slow and inefficient. Their main applications are classification and clustering. CNNs are supervised learning algorithms which are designed to learn the spatial hierarchies of features automatically through backpropagation by using multiple building blocks, such as convolution layers, pooling layers, and fully connected layers. They are easy to train and implement, and they are efficient in pre-training and feature extraction. However, they need large amounts of memory to store their intermediate results, and their main uses are in video and image recognition, natural language processing, image classification, etc. RNN is a supervised \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Reference No.** & **Algorithm Used** & **Domain** & **Accuracy Rate** \\ \hline
[30] & Back propagation & Sonar target recognition & Nearly 100\% \\ \hline
[31] & Stacked autoencoders & COVID-19 digital images recognition & 80\% \\ \hline
[15] & Deep Boltzmann machine & Classification of hyperspectral medical images for the diagnosis of cancerous regions & 95.5\% \\ \hline
[16] & Deep belief networks & Classification of normal and COVID-19 patients using medical images of chest X-rays & 90\% \\ \hline
[18] & CNN & For diagnosis of Alzheimer’s disease by a voxel-based hierarchical feature extraction method & 97\% \\ \hline
[26] & CNN & For motor imagery classification based on sorted blind source separation, continuous wavelet transform, and convolutional neural network & 94.66\% \\ \hline
[25] & CNN & To detect COVID-19 & 99\% \\ \hline
[25] & DNN & To detect COVID-19 & 99.7\% \\ \hline
[25] & SVM & To detect COVID-19 & 99.68\% \\ \hline
[25] & KNN & To detect COVID-19 & 93.41\% \\ \hline
[36] & DBN and convolutional DBN & To detect COVID-19 in chest X-ray images & 99.93\% \\ \hline
[37] & SVM and NN & To detect dementia diseases & 90\% \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Deep learning algorithms used in different domains with accuracy. learning algorithm which can relearn from old data as new data are acquired, and this is beneficial in for time series. The nature of RNNs makes their calculations slow. RNNs have many applications, such as machine translation, speech, and voice recognition, handwriting recognition, etc.