# A Review of Deep Learning Algorithms and Their Applications in Healthcare
## 2 Related Works  
Deep learning has been widely implemented in various areas in health informatics, such as bioinformatics, medical imaging, pervasive sensing, medical informatics, and public health [9]. An analysis of deep learning algorithms reported in the literature related to the health sector, especially COVID-19 prediction, classification, and detection strategies is presented below. Deep learning is able to be employed reliably with respect to physiological signals obtained via electromyogram (EMG), electroencephalogram (EEG), electrooculogram (EOG), or electrocardiogram (ECG). For these purposes, the most common algorithms used in processing these physiological signals are RNNs and one-dimensional CNNs [9]. The author of [12] proposed a sonar target recognition. Two-layer backpropagation networks are trained to distinguish between reflected sonar signals from rocks and metal cylinders at the bottom of the Chesapeake Bay. A total of 60 input units and 2 output units are involved. The input pattern is based on the Fourier transform of the raw time signal. The best performance was obtained with 12 hidden units (nearly 100% training set accuracy). The authors of [30] proposed a solution for medical practitioners handling a COVID-19 crisis that implements protocols of investigation for the first patient infected by this disease, which will also be beneficial to epidemiologists for making a decision about virus spread. This paper presents a framework based on deep learning (specifically, stacked auto-encoders in medical image retrieval) in its feature-extraction phase. During retrieval tasks, this method reduces the image's size to a small, compact representation, allowing for faster image comparisons. In a content-based image retrieval system, the importance of the auto-encoder approach can be seen, since it achieves good recognition accuracy at 80% for COVID-19 digital images, given the capability of the stacked encoders. Autoencoder algorithms have also been developed for cancer diagnosis. Sparse autoencoders are presented for microaneurysm detection in fundus images as a part of a diabetic retinopathy strategy. Additionally, various autoencoder-based learning methods have been developed for use in Alzheimer's disease prediction based on functional magnetic resonance images (FMRI), 3D brain reconstruction, cell clustering, and human behavior monitoring [31]. In [13], researchers examined various autoencoding architectures that integrated multiple types of data (e.g., multi-omic and clinical) from cancer patients. They explored these approaches and provided a clear framework for developing systems which can be used for investigating cancer characteristics and translating some of the results into clinical applications. Using these networks, they described how to design, build, and apply integrative analyses of heterogeneous data on breast cancer. These approaches yield accurate and stable diagnoses by producing relevant data representations. RBM architectures can be applied to manifold brain MRIs used in Alzheimer disease detection, segmenting multiple sclerosis lesions in multi-channel 3D MRIs, assigning diagnosis automatically according to the clinical status of patients, suicide risk prediction for mental health patients using low-dimensional approaches to the medical aspects embedded in electronic health records (EHRs), and determining photoplethysmography signals for health monitoring [14]. A deep learning framework for the classification of complex hyperspectral medical images is presented in this study. To accomplish this, a deep Boltzmann machine (DBM) architecture of the bipartite structure was developed as an unsupervised generative model. The implementation is based on a three-layer unsupervised network with a backpropagation structure. In the present dataset, image patches are collected and classified into two classes, namely, non-informative and discriminative classes. The spatial information is used for classification and spectral-spatial representations of class labels are created.