# A Review of Deep Learning Algorithms and Their Applications in Healthcare
## 5 Applying Deep Learning Algorithms in Healthcare  
Various deep learning algorithms are used in practical applications in the current situation with the COVID-19 pandemic. Deep learning has shown itself to be useful in healthcare for governments worldwide. These applications provide image-processing capabilities, classification, or clustering, or predict when life will resume as before. As of the time of writing, it is reported that more than 213 million patients have been diagnosed with COVID-19, and 4.4 million of these cases have passed away, according to the Coronavirus Resource Centre at Johns Hopkins University [85]. This pandemic poses a dire threat to human civilization. In the pre-COVID-19 era, deep learning was not viewed as a sustainable algorithm for health informatics due to the large amount of training data and computational resources it requires, compared to other algorithms that do not require similar efforts and tuning [30]. Deep learning techniques were constantly negatively viewed due to their lack of interpretability [14]. However, COVID-19 has created a need for robust research in order to find the best classification, screening, and diagnostic measures. Search code strategies were used to track the progress of machine learning and deep learning in predicting, detecting, and diagnosing COVID-19. Convolutional neural networks, deep neural networks, and support vector machine algorithms have exhibited accuracies of up to 99% while detecting the virus [86]. In the medical field and healthcare, deep learning was used in detecting and differentiating among COVID-19, viral pneumonia, and healthy chest X-rays in patients using image-processing capabilities [87]. The COVID-DeepNet system has been proposed for determining COVID-19 in chest X-ray (CX-R) images [36]. This system helps radiologists who have experience in understanding the images quickly and accurately [36]. The results taken from two dissimilar methods rely upon the combination of a convolutional deep belief network and deep belief network, trained from the beginning utilizing a big dataset [36]. The developed system appears to offer precision and efficiency and can be utilized to identify COVID-19 by applying early diagnosis. Additionally, this system can be used to follow-up the treatment, with each image taking less than 3 s to be decided upon [36]. Diagnosis, techniques such as Covid-Net CNN, ConoNet CNN, Bayes SqueezeNet, and CoroNet AutoEncoders, have performed superiorly with high accuracies. Other deep learning algorithms have diagnosed COVID-19 after being used on CT-scan datasets, such as the WOA-CNN, CRNet, and CNNs [88]. An entirely automatic system of deep learning for the diagnosis of COVID-19, and analysis of prognosis, has utilized computed tomography [36]. From seven cities or provinces, 5273 patients along with their computed tomography images have been gathered [16]. For pre-training the deep learning system, 4106 patients along with their tomography images have been used, enabling the system to learn the features of the lung [16]. Subsequently, 1266 patients from six cities or provinces have been registered with the intention of training and validating externally the system performance of deep learning [16]. A total of 924 out 1266 patients had COVID-19 (471 were followed-up for \(>\) days) [16]. In particular, 342 out of 1266 patients had other pneumonia [16]. The system of deep learning accomplished satisfactory performance in distinguishing COVID-19 from viral and other pneumonia within the four sets of external validation [16]. In addition, the system of deep learning has the ability to group patients into low and high risks whose time of stay at hospital has important dissimilarities [16]. The rapid diagnosis of COVID-19 and determining the patients with high risks can be achieved using deep learning, which can help in enhancing the medical resources and also to help the patients before they will be in critical states [16].