# A Review of Deep Learning Algorithms and Their Applications in Healthcare
## 5 Applying Deep Learning Algorithms in Healthcare
In this article, they designed a weakly supervised deep learning architecture for fast and fully-automated detection and classification of COVID-19 cases using retrospectively extracted CT images from multiple scanners and multiple centers. It can distinguish COVID-19 cases accurately from CAP (Community Acquired Pneumonia) and NP(Non-Pneumonia) patients. It can also spot the exact position of the lesions or inflammations caused by the COVID-19, and can also provide details about the patient severity in order to guide the following triage and treatment. Experimental findings have indicated that the proposed model achieves high accuracy, precision and promising qualitative visualisation for the lesion detections [96]. This article provide a solution for recognizing pneumonia due to COVID-19 and healthy lungs (normal person) using CXR (chest X-ray) images. They used the state-of-the-art technique, genetic deep learning convolutional neural network (GDCNN). It is trained from the scratch for extracting features for grouping them into COVID-19 and normal images. The proposed method do better compared to other transfer learning techniques. Classification accuracy of 98.84%, the precision of 93%, the sensitivity of 100%, and specificity of 97.0% in COVID-19 prediction is attained [97]. As long as the number of patients is very high and the level of radiological expertise required is low, deep learning-based recommender systems can be of great assistance in diagnosing COVID-19. The study examined four different deep CNN architectures on chest X-ray images of COVID-19 patients for diagnosis recommendation. From all of the models, the Mobilenet model comes out on top. Based on the results, CNN-based architectures have the potential to diagnose COVID-19 correctly. Transfer learning plays a key role in improving detection accuracy. Further fine-tuning of these models may improve their accuracy [98]. In this paper they presented a clinical decision support system for the early detection of COVID-19 using deep learning based on chest X-ray images. For this, they developed an architecture made up of three stages. The first stage includes pre-processing of input images followed by data augmentation. The second stage includes feature extraction followed by learning. Finally, the third stage produces the classification and prediction process with a fully connected network of several classifiers. The proposed deep learning algorithm provided an AUC of 0.97 for internal validation and 0.95 for external validation based on the number of chest Xray images with an accuracy of 92.5% and 87.5% respectively [99]. This paper addresses how AI provides safe, accurate and efficient imaging solutions in COVID-19 applications. Two imaging techniques, i.e., X-ray and CT, are used to show the effectiveness of AI-empowered medical imaging for COVID-19. Imaging only gives partial information about patients with COVID-19. So, it is important to integrate imaging data with both clinical manifestations and laboratory examination results to help better screening, detection and diagnosis of COVID-19. AI will demonstrate its natural capability in fusing information from these multi-source data, for performing accurate and efficient diagnosis, analysis and follow-up [96]. In this paper, the significance of the AI-driven tools and suitable training and testing models have been discussed. AI-driven tools are required to be implemented from the beginning of data collection, in parallel with the experts in the field, where active learning needs to be implemented. During the decision-making process, multiple data types should be used rather than just one in order to increase confidence. As part of active learning, multitudinal and multimodal data have been discussed [100]. They presented an artificial intelligence (AI) system for rapid COVID-19 detection and extensively analyzed the CTs of COVID-19 based on Artificial Intelligence.