The COVID-19 epidemic analysis and diagnosis using deep learning: A systematic literature review and future directions

Abstract

Since December 2019, the COVID-19 outbreak has resulted in countless deaths and has harmed all facets of human existence. COVID-19 has been designated an epidemic by the World Health Organization (WHO), which has placed a tremendous burden on nearly all countries, especially those with weak health systems. However, Deep Learning (DL) has been applied in several applications and many types of detection applications in the medical field, including thyroid diagnosis, lung nodule recognition, fetal localization, and detection of diabetic retinopathy. Furthermore, various clinical imaging sources, like Magnetic Resonance Imaging (MRI), X-ray, and Computed Tomography (CT), make DL a perfect technique to tackle the epidemic of COVID-19. Inspired by this fact, a considerable amount of research has been done. A Systematic Literature Review (SLR) has been used in this study to discover, assess, and integrate findings from relevant studies. DL techniques used in COVID-19 have also been categorized into seven main distinct categories as Long Short Term Memory Networks (LSTM), Self-Organizing Maps (SOMs), Conventional Neural Networks (CNNs), Generative Adversarial Networks (GANs), Recurrent Neural Networks (RNNs), Autoencoders, and hybrid approaches. Then, the state-of-the-art studies connected to DL techniques and applications for health problems with COVID-19 have been highlighted. Moreover, many issues and problems associated with DL implementation for COVID-19 have been addressed, which are anticipated to stimulate more investigations to control the prevalence and disaster control in the future. According to the findings, most papers are assessed using characteristics such as accuracy, delay, robustness, and scalability. Meanwhile, other features are underutilized, such as security and convergence time. Python is also the most commonly used language in papers, accounting for 75% of the time. According to the investigation, 37.83% of applications have identified chest CT/chest X-ray images for patients.

A privacy-aware method for COVID-19 detection in chest CT images using lightweight deep conventional neural network and blockchain

“…When all values in a Confusion Matrix are added together, the ratio of True Negatives to True Positives is calculated. The number of images correctly classified in this equation is , and the total number of images is denoted by [ 32 , 33 ]. …”

“…The proportion of True Positive predicted observations compared to all positively predicted observations. Also, represents the sum of all true positives, while represents all false positives [ 32 , 33 ]. …”

“…Also, the F1 Score is an overall performance metric made up of recall and precision. The harmonic mean of precision and recall represents it [ 32 , 33 ]. …”

“…As previously stated, ML approaches are an important component of AI, which alludes to the intelligence demonstrated by machines [ 30 , 31 ]. Furthermore, DL approaches are scalable ML extensions that help to enhance the structure of learning methods and make them easier to utilize in difficult issues [ 32 , 33 ]. DL is widely used to produce complex patterns with massive amounts of data in a basic context [ 34 ].…”

Human Centered Decision-Making for COVID-19 Testing Center Location Selection: Tamil Nadu—A Case Study

“…Researchers in [ 5 ] developed software called VECTOR to process the lung sounds for identifying the presence of interstitial pneumonia. Machine learning techniques [ 6 , 7 ] and deep learning techniques [ 8 , 9 ] are also employed for the early diagnosis of COVID. Natural products based on flavonoids for COVID are suggested [ 10 ].…”