# A Review of Deep Learning Algorithms and Their Applications in Healthcare
## 1 Introduction  
Deep learning is a machine learning technique that teaches computers and devices logical functioning. It is inspired by the structure of the human brain. Deep learning originated as artificial neural networks (ANNs) and has developed far more efficiency after decades of research and development compared to the other machine learning algorithms [1]. In the early stages of the development of neural networks, researchers aspired to create a system that mimicked the functions of the human brain. In 1943, [PERSON] attempted to explain how the brain could create highly complex patterns by using neurons [2]. They invented the MCP (McCulloch Pitts neuron) model, which had a significant impact on the development of artificial neural networks. They implemented threshold logic in their model to simulate the human thought process but not to learn. Since then, the development of deep learning has been slow but steady, with several significant milestones along the way. In 1958, [PERSON] invented the perceptron, the first prototype of our modern neural networks, which consisted of two layers of processing units that are commonly thought of as a basic mechanism for recognizing simple patterns [3]. Rather than undergoing further development and research, the evolutionary history of neural networks and artificial intelligence underwent a winter after 1969, after the MIT professors [PERSON] and [PERSON] proved the limitations and failings of the perceptron [4]. The standstill was broken when the backpropagation algorithm emerged in 1974, invented by [PERSON] [5]. It is considered a significant milestone in neural network development. In 1980, [PERSON] [6] introduced the \"Neocognitron,\" which is regarded as the ancestor of convolutional neural networks, followed by the Boltzmann machine, invented by [PERSON] et al. in 1985 [1], and the recurrent neural network, invented in 1986 by [PERSON] [7]. In 1998, [PERSON] introduced the convolutional neural network with backpropagation for document analysis [8]. Deep learning has made major progress since 2006, when [PERSON] introduced the concept of deep belief networks (DBNs) [9; 10]. These involve a two-stage plan: pre-training and fine-tuning. This technique allowed researchers to train neural networks much more deeply than previous methods. Deep learning algorithms aim to draw similar conclusions as humans do by continuously analyzing data according to a given logical structure. Deep learning allows machines to manipulate images, text, or audio files like humans to accomplish human-like tasks. To achieve this, deep learning uses a multi-layered structure of algorithms called neural networks. As the name suggests, deep learning involves going deep into multiple layers of a network, which include a hidden layer. As one goes deeper, more complex information is extracted. Deep learning relies on iterative learning methods, which expose machines to very large datasets. It helps computers to learn to identify traits and adjust to changes. Machines are able to learn differences among datasets, understand the logic, and reach reliable conclusions after repeated exposures [1]. A review on deep learning has been presented in [11]. In [11], the following information is provided: Computational models consist of multiple layers for processing. They are permitted to learn data representations with various levels for abstraction by deep learning. By using these techniques, the state-of-the-art has been enhanced in several fields, including visual object recognition, speech recognition, genomics, and discovery of drugs, along with plenty of others. Backpropagation is applied by deep learning in order to find the complex structure within a huge dataset, and this for specifying the way that internal parameters of machine have to be altered by the machine, and these are employed to calculate the representation within every layer depending on the representation within the prior layer.