# A Review of Deep Learning Algorithms and Their Applications in Healthcare
## 5 Applying Deep Learning Algorithms in Healthcare
In order to classify the image tissue, [89] utilized SegNet and U-NET as two recognized networks of deep learning. U-NET is a tool of medical segmentation [89], and SegNet is the network of scene segmentation [89]. SegNet and U-NET were used as binary segmentors in order to distinguish between infected and healthy lung tissue [89]. Moreover, the two networks can be used as multi-class segmentors for the purpose of learning the infection within the lung [89]. Every network used seventy-two images for training [89], ten imagesfor validation [89] and eighteen images for testing [89]. The results showed that SegNet is capable to differentiate between healthy and infected tissues. In addition, U-NET provided better results based on multi-class segmentor [89]. Additionally, using models that apply convolutional neural networks and recurring neural networks, applications can be created to predict vaccination patterns in the future [89]. Deterministic and stochastic recurrent neural networks were used to predict the geographic spreading of the active virus using unsupervised learning methods so as to plan vaccine distribution among the USA, as a case study [90]. Machine learning helps governments and health ministries to prepare and schedule the dosages required for the public. In addition, algorithms have helped to forecast the case numbers and mortality statistics in many countries. Deep learning has improved the accuracy of predictions, enabling improved data-driven decisions regarding easing or enforcing lockdowns [91]. A case study was conducted in India that took no external factors that could affect the rate of spread to predict lockdown extension time. A linear regression model was used to predict how long lockdowns should last in order to eradicate COVID-19 from India [84]. The authors of [87] discussed the ways that deep learning assisted in the COVID-19 pandemic and presents guidelines for upcoming research on COVID-19. The authors of [86] provided applications of deep learning in different fields, such as computer vision, natural language processing, epidemiology, and life sciences. The authors of [92] described the differences of the applications of big data and methods for building the tasks for learning. In addition, ([91], p. 19) introduces deep learning's limitations for application during COVID-19. The limitations are generalization metrics, interpretability, the privacy of data, and using limited labelled data for learning. Deep learning algorithms can be used to forecast the number of COVID-19 cases and death cases. The multivariate CNN algorithm outperformed the LSTM in terms of validation accuracy and forecasting consistency. CNN has been suggested for long-term forecasting in the absence of seasonality and periodic patterns in time series datasets [90]. The paper mentioned that DL techniques have a significant impact on early detection of COVID-19 with high accuracy rate. Most of the studies used deep learning to detect COVID-19 cases in early stage based on different diagnostic techniques. The most widely used techniques are convolutional neural network (CNN) and transfer learning (TL) [93]. The paper detailed that the use of AI in COVID-19 investigation can be summarized in terms of clinical image examination, drug design and pandemic prediction against coronavirus. The study revealed that in a considerable number of patients with suspected COVID-19 pneumonia, CT should be examined following CXR, potentially causing impairment in the absence of pre-defined diagnostic work-up criteria. The DNN architectures are built from the ground up instead of applying transfer learning techniques [94]. Deep Learning applications to detect the symptoms of COVID-19, AI based robots to maintain social distancing, Block chain technology to maintain patient records, Mathematical modeling to predict and assess the situation and Big Data to trace the spread of the virus and other technologies. These technologies have immensely contributed to curtailing this pandemic [95].