# A Review of Deep Learning Algorithms and Their Applications in Healthcare
## 2 Related Works
The accuracy, false-positive predictions, and sensitivity are calculated for the fully-connected network based on the labelled classes. With the proposed cognitive computation technique, an accuracy of 95.5% and sensitivity of 93.5% were achieved. The DBM model provides a significant improvement in the computer-aided diagnosis of cancerous regions in HIS (hyperspectral imaging) images. This method separates two types of image patches, informative and non-discriminative, in the first phase. By calculating the accuracy and success rate of DBM in the second phase of learning, the performance was confirmed. This method was 6.1% more accurate than CNN. The results showed that the unsupervised DBM was best suited for identifying cancer regions from complicated 3D hyperspectral images [15]. DBNs are used in the modeling of compound-protein interactions, anomaly detection, biological parameter monitoring, obstacle detection, sign language recognition, detecting lifestyle diseases, and modeling infectious disease epidemics [31]. In [16], deep belief networks were used to classify normal and COVID-19 patients using medical images of chest X-rays. The proposed system successfully identifies COVID-19 cases with a 90% accuracy rate. The diagnosis is usually based on the presence of a white patchy shadow in the lungs. By using a Gaussian filter (to remove the noise), it is able to detect these diseases through these medical images. The medical images are then separated by separating the important section of the lungs from the others. Threshold-based segmentation is performed to separate gray pixels from the others, and the number of gray pixels in lungs affected by COVID-19 is very low in comparison to normal lungs. CNN algorithms can be employed in the prediction of risk of osteoarthritis by using the automatic segmentation of knee cartilage MRIs, when using retinal fundus photographs to detect diabetic retinopathy, in dermatologist-level classification of skin cancer, in congestive heart failure prediction, for predicting chronic obstructive pulmonary disease using longitudinal EHRs, for predicting the quality of sleep using physical activity data during awake time, in electroencephalogram analysis, and for estimating the prevalences of various chromatin marks [14]. The early stages of Alzheimer's disease (AD) are associated with mild cognitive impairment (MCI). For the early diagnosis of Alzheimer's disease, some authors proposed a new voxel-based hierarchical feature extraction method (VHFE). The entire brain was divided into 90 regions of interest (ROIs) using an automatic anatomical labeling template (AAL). Theydivided the uninformative data by selecting the informative voxels in each ROI, using a baseline of the voxels' values to make a vector. On the basis of the correlations between voxels of different groups, the features for the first stage were selected. In order to learn deeply hidden features, the brain feature maps made up of the voxels of each subject were fed into a convolutional neural network (CNN). The results show that the proposed method is robust, having a promising performance in comparison to the current state-of-the-art methods [18]. In [32], processing data that exhibit natural spatial invariance (e.g., images whose meanings do not change under translation) is shown to have grown to be central in the field (CV) [32]. The use of deep learning systems could help physicians by providing second opinions and flagging concern areas. In object-classification, CNNs learn to classify objects contained an images and have achieved human-level performance. They are initially trained on a massive dataset, unrelated to the task of interest, to learn the natural statistics in images (curves, straight lines, colorations, etc.) before being further fine-tuned on a much smaller dataset related to the task of interest (e.g., medical images). The higher-level layers of the algorithm are retrained to distinguish between diagnostic cases. CNNs demonstrated strong performance in transferring learning [33].