# A Review of Deep Learning Algorithms and Their Applications in Healthcare
## 2 Related Works
[24] proposed a sequence decomposition and deep learning integrated prognostic approach for the RUL predictions of LIBs. To separate the local fluctuations and the global degradation trend from the battery aging data, complementary ensemble empirical mode decomposition and principal component analysis are applied. Since the long- and short-term memory (LSTM) fully connected (FC) structure makes good use of offline and online data information, an LSTM neural network combined with FC layers was designed as a transfer learning model. The hyper-parameter optimization and fine-tuning strategy of the model were developed based on offline training data. The illustrative results demonstrate that the proposed approach can achieve adaptive, accurate, and robust predictions for both RUL and capacity trajectory. By analyzing routine computed tomography, the authors proposed an automatic deep learning system for COVID-19 diagnostics and prognostics. Deep learning is a convenient tool for diagnosing COVID-19 and identifying patients at risk, which may aid in resource optimization and early prevention of disease before severe symptoms occur. CT scanning can be acquired within minutes if a patient is suspected. Once this deep learning system has been applied, it is possible to predict whether a patient has COVID-19. The deep learning system also predicts the patient's prognostic situation if they are diagnosed with COVID-19, which can be used to identify high-risk patients who require urgent medical attention. The system is fast and does not require human-assisted image annotation, increasing clinical value and enhancing robustness. Deep learning takes less than 10 s to produce a prognostic and diagnostic prediction for a chest CT scan of a patient [16]. The authors of [25] reported on how experts (medical or otherwise) and technicians have used deep learning techniques to combat the COVID-19 outbreak. The rapidly development of artificial intelligence (AI) in the area of medical image analysis has also helped to combat COVID-19 by providing high quality diagnostic results and by reducing or eliminating the need for manpower when employed. For COVID-19 diagnoses using CT and X-ray samples, deep-learning-based support systems have been developed. Several of these systems have been introduced using customized networks, and some were derived from pre-trained models with transfer learning. Similarly, machine learning and data science are also actively used for COVID-19 diagnosis, prognosis, prediction, and outbreak forecasting. In addition, big data, smartphone technology, and the Internet of Things (IoT) enable innovative solutions to combat the spread of COVID-19. Researchers have used multiclass classification methods to distinguish images of patients with infectious diseases, such as COVID-19 viral pneumonia, bacterial pneumonia, fungal pneumonia, SARS, MERS, influenza, and tuberculosis, from those of healthy people. Multiclassifiers are more accurate than binary classifiers in detecting COVID-19 cases. CNNs and DNNs (deep neural networks) are the most significant classification methods for COVID-19 detection, followed by SVM, random forest, KNN (K nearest neighbors), and LSTM. Furthermore, the CNN is the most widely used classifier for COVID-19 diagnosis regarding machine learning/deep learning techniques. Machine learning/deep learning-based approaches can significantly enhance intelligent diagnosis systems, which has promise for healthcare professionals seeking to detect the virus quickly and reliably. Furthermore, they will eliminate manual errors made by physicians and radiologists during diagnosis. Additionally, they will facilitate more accurate and time-efficient diagnoses. Researchers have used X-rays, CT images, RT-PCR, and clinical blood data to investigate COVID-19's prognosis and anomalies. In the most recently mentioned study, the highest accuracies in detecting COVID-19 were achieved by CNN, DNN, SVM, KNN (K nearest neighbors), and R.F, with 99%, 99.7%, 99.68%, 93.41%, and 95%, respectively.