# A Review of Deep Learning Algorithms and Their Applications in Healthcare
## 1 Introduction
RNNs are mainly used for sequential data, such as speech and text, whereas CNNs are used for the processing of images, speech, audio, and video. In recent years, a large number of studies, such as [9; 10; 11; 12], have made use of deep learning and demonstrated its capabilities in many respects, including healthcare and health informatics, such as in bioinformatics, medical imaging, pervasive sensing, medical informatics, and public health [13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 24; 25]; motor imagery classification [26], for the performance improvement of sensorless FOC [27]; and a new method developed for a retrofitted self-tuned controller with FPGA [28] and for a self-tuning neural network PID [29]. This paper aims to provide a literature review on different well-known deep learning algorithms. We highlight their capabilities, suitabilities, shortcomings, and applications, which many researchers in this field can benefit from knowing. Moreover, critical analyses of different deep learning algorithms are provided, focusing in particular on their advantages, disadvantages, and applications, whether supervised or unsupervised. In addition, the application of deep learning algorithms to healthcare is also introduced. The main contribution of this paper is to systematically review several popular deep learning algorithms such as backpropagation, autoencoders, variational autoencoders, restricted Boltzmann machines, deep belief networks, convolutional neural networks, recurrent neural networks, generative adversarial networks, capsnets, transformer, embeddings from language models, bidirectional encoder representations from transformers, and attention in natural language processing. This review discusses several challenges of deep learning. This literature review also demonstrates the advantages, disadvantages, and applications of these the algorithms, and identifies whether they are supervised or non-supervised. Moreover, the applications of deep learning algorithms in healthcare are also presented; for instance, details of how deep learning algorithms can be used to fight COVID-19 pandemic are provided in this paper. The directions of future works are also given in this paper. The researchers in the deep learning field interested in using deep learning algorithms in healthcare can obtain benefits from this review, which provides detailed information about each algorithm. In addition, beginners and new researchers in the deep learning field can read this review as a starting point to gain a comprehensive understanding of each given deep learning algorithm, helping them to attain the knowledge that they need to solve problems related to deep learning and to design and develop deep learning algorithms. The paper is organized as follows: Works related to deep learning algorithms are presented in Section 2, followed by a comparison of the accuracy of deep learning algorithms applied in different domains. Artificial neural networks and several well-known deep learning algorithms are introduced in Section 3. A comparison between these deep learning algorithms based their advantages, disadvantages, applications, and whether they are supervised or unsupervised is presented in Section 4. The application of deep learning algorithms to healthcare is given in Section 5. Several challenges of deep learning are displayed in Section 6. Future directions of deep learning are provided in Section 7. Finally, conclusions and future works are introduced in Section 8.