# A Review of Deep Learning Algorithms and Their Applications in Healthcare
## 5 Applying Deep Learning Algorithms in Healthcare
They evaluated the system using large datasets consisting of more than 10 thousand CT scans from the COVID-19, influenza-A/B, community acquired pneumonia (CAP), and other subjects. On a test cohort of 3199 scans, the deep convolutional neural network-based system achieved an area under the receiver operating characteristic curve of 97.81%. This AI system outperformed all five radiologists in a reader study by two orders of magnitude when facing more challenging tasks [101]. COVID-19 is a new disease announced on 11 February 2020. The major symptoms of COVID-19 are fever, breathing difficulty, dry cough, headache, runny nose, nasal blockage, body pain, and throat pain [102]. The disease can easily transfer to others through droplets. Respiratory droplets >5-10 \(\upmu\)m can spread the virus and are spread easily through direct contact compared to droplet nuclei with particle sizes <5 \(\upmu\)m. The transmission of the droplet occurs within 1 m direct contact with a COVID-19-infected person. As of 22 May 202, there were 4,995,996 confirmed cases of coronavirus in over 216 countries and 3,27,821 confirmed deaths [102]. The COVID-19 pandemic has exposed the vulnerability of healthcare services worldwide. There is a clear need to develop computer assisted diagnosis tools to provide rapid and cost-effective screening to identify SARS-CoV-2. CBC, chest X-ray, Polymerase Chain Reaction (PCR), chest CT, and the IgM/IgG combo test are the diagnosis methods that are generally utilized for COVID-19. Among these, CT scans produce the best performance in COVID-19 diagnosis [103]. The artificial intelligence (AI) applications related to COVID-19 are involved in medical imaging, drug development, lung damage delineation, cough sample analysis, etc. AI algorithms can take CT images of patients with clinical symptoms to diagnose COVID-19 result rapidly. A CNN is used to classify the medical image and diagnose the disease as pneumonia or COVID19. A linear support vector machine, VGG16, and Inception V3 were used in one study [104]. Supervised machine learning algorithms such as SVM, random forest, and I Bayes are mostly utilized to predict the disease easily. The random forest provided the highest accuracy in 9 of 17 studies, with 53% overall. Among these studies, three SVMs produced the best classification for prediction of disease [105]. Automatic investigative systems based on AI and ML devices have been developed to detect the coronavirus accurately and rapidly to protect healthcare workers in direct contact with COVID-19 patients [106]. DeTraC DCNN is utilized to classify chest X-ray images (suspected COVID-19) accurately. It achieved 95.12% accuracy in the finding of coronavirus X-ray images [104]. Extracting CT image features using a deep learning model could provide clinical diagnosis to save time [107]. One of the challenging issues is distinguishing COVID-19 coughing sounds from non-COVID-19 coughing sounds. The AI4 COVID19 application is used to record cough sound samples for 2 s. Matching the voice samples with coronavirus patients and non-COVID-19 patients was done with 90% accuracy [108]. Deep learning techniques are utilized for computed tomography (CT) images to distinguish between coronavirus and pneumonia [109]. COVID-19 and pneumonia X-ray image data classes were created using a fuzzy coloring technique for a preprocessing step. The preprocessing datasets were trained with DL models such as SqueezeNet and MobileNetV2. With up to 99.27% accuracy, a support vector machine is used to extract and classify the data features of COVID-19 [110]. Ten convolution neural network techniques were utilized to distinguish between coronavirus and non-coronavirus groups. Among all these networks, Xception and ResNet-101 both achieved high performance area under the curve (AUC) of 0.994. The radiologist's performance was moderate with an AUC of 0.873 [111]. RestNet-100, a classifier made of a convolutional neural network (RCNN), combined with logistic regression (LR), was used to identify COVID-19.