# A Review of Deep Learning Algorithms and Their Applications in Healthcare
## 6 Challenges of Deep Learning
### Data Management Challenges  
Reference [156] presents several data management challenges that maybe be occurred in every phase of the following phases: data collection, data exploration, data preprocessing, dataset preparation, data testing, deployment and postal deployment. These challenges can be faced by the people who are executing the deep learning in real life applications [156]. Data collection can be defined as a systematic procedure of collecting data from different sources that are related to the subject [156]. In data collection, the following challenges can occur [156]:  
Lack of metadata. The people who work on such projects indicate that lack of metadata leads to ambiguity and a lack of understanding of data. Therefore, these people demand metadata, perhaps also as are not experts in the area where the elements of deep learning are executed. For the data granularity challenge, beyond the process of data gathering is aggregating the data. Methods of data aggregation can cause the deletion of important data, and therefore data granularity is missed. The shortage of diverse samples challenge is this: deep neural networks should train on all potential samples, including normal instances and rare instances. Otherwise, the deep neural network will not have success when previously unseen data appear. Plenty of companies have gathered huge amounts of data, but they fail to collect rare instances, which leads to deep learning models that are not trained on the rare data. For the purposes of sharing and tracking techniques, information needs to be shared among the people who are working on deep neural networks. However, a common medium or channel to share data does not exist. Tracking techniques are a significant measure of confirming data quality. However, because a lack of resources and time, frequently the concentration upon data quality is insufficient, and thus, data quality is often weak. The performances of deep learning models are largely influenced by the quantity of the training data. Clear knowledge on the ways to gather and store data based on General Data Protection Regulations (GDPR) is not possessed by small companies. Moreover, their data gathering is not performed efficiently due to non-existing protocols or frameworks. Data exploration can involve different tasks, such as analyzing the distributions of dissimilar datasets, testing the quantity of outliers, and testing how data can be connected with each other. Specifying the missing data and creating a dataset that can be applied to the model are also forms of data exploration [156]. The challenges of data exploration are as follows [156]: Statistical understanding of some data distributions is considered a challenge even when one has adequate knowledge about statistics. Deduplication complexity is a challenge due to datasets normally having duplicates; and consider a complex task wherein datasets have both duplicates and deduplication as a complex. Another challenge is the heterogeneity of data. Heterogeneity of data can be shown through the variety of size, format, and encoding methods. Data preprocessing is the task of treating all the problems of raw data prior to building datasets. Data can be gathered from different sources, and therefore, the data can contain incorrect values, missing values, and perhaps different formats. These issues are to be resolved prior to the use of deep learning models [156]. The challenges of data preprocessing are as follows [156]: One of the challenges during data preprocessing is dirty data. Dirty data includes missing and error-laden datasets, and also datasets with incorrect data formats. Another challenge is managing categorical data. Categorical data are split into groups, so such data have label values and not numerical values. Categorical data have two types: nominal and ordinal data. The models of deep learning request the input variables to be numeric and not text. Therefore, text values are required to be converted into numeric values. Managing sequences in data is another challenge.