# COVID-19 Detection from Radiographs: Is Deep Learning Able to Handle the Crisis?
## 5 Open Problems and Challenges  
**Lack of Significant Datasets**: Data are vital for the progress of any field, and deep learning is no exception. Similarly, the identification of the specific anomalies requires significant annotated datasets. Although there are some available datasets for COVID-19, these are not significant enough for confident detection of an infection underway. Therefore, it is crucial to have large annotated datasets to accurately and appropriately detect the condition.  
**Medical Experts Input**: The system's accuracy is dependent on the input of the experts in the research field. Although deep learning systems provide decent accuracy in identifying or classifying the infection, medical expert opinions are required on the outcome of the model to make sure that prediction is correct. The medical expert should also be consulted to determine the challenging cases and variants of the virus for the model's robustness. Overall, the involvement of medical experts and their input should be at the heart of this research.  
**Lack of Variants Identification**: Since the pandemic's start, everyone knew only one name: coronavirus. However, as time passed, more information about the infection came to light about the virus variants and strains. The infection capability and severity change depending on the strain; some are infectious, while others are dangerous. The identification and classification of these strains are vital so that the system can identify the strain type and warn about the challenges posed by infection.  
**Models' Efficiency**: The efficiency of the models is another aspect neglected in COVID-19 research, as the focus thus far has been on the effectiveness rather than the efficiency. Real-time diagnosis is essential, along with accuracy, if the models need to be deployed for screening on devices. Therefore, it is desired to have research dedicated to training efficient models.  
**Benchmark Deficiency**: Benchmarks play an important role in advancing the field and identifying the state-of-the-art methods from the crowd, as there is a significant effort in  
Figure 13: Samples from the COVIDx dataset to show the feature attention. All input images contain COVID-19 cases. The sequence shown in the figure is (**a**) input images, (**b**) ResNet, (**c**) DenseNet, (**d**) GoogleNet, (**e**) ResNeXt, (**f**) MNASNet, (**g**) EfficientNet, and (**h**) ShuffleNet.  
COVID-19 research. The lack of benchmarks made it hard to isolate the excellent investigation, analysis, and methods.  
**Generalization**: The proposed models' generalization for COVID-19 case detection, classification, and identification is vital due to the variants and strains emerging. The trained model should have the ability to accurately detect and classify cases without retraining the whole system in case of a new variant. The best approach would be to apply attention to the details specific to the virus type. Moreover, it should be noted that the dataset quality may also influence the models' generalization and robustness. Hence, a step toward generalization is to utilize pretrained models with attention mechanisms.  
**Integrating Input Data Types**: COVID-19 has been identified or detected from a single source such as an X-ray or CT. However, utilizing more information, including the body temperature, will help improve the performance and reliability of the methods. Furthermore, employing multiple data types helps predict the relationship between COVID-19 and the most robust combination for early detection.