article · Viruses
The global spread of COVID-19 created an urgent need for rapid infection detection, but deep learning models designed to diagnose the disease often suffer from a lack of reliable training data. To address this limitation, two data-augmentation models were developed to improve the learnability of Convolutional Neural Network and Convolutional Long Short-Term Memory deep learning architectures. Experimental evaluations showed that incorporating data augmentation enhanced detection accuracy, lowered logarithmic loss, and shortened testing times compared to baseline deep learning setups. In addition, the augmented deep learning models demonstrated an average accuracy increase of between 4% and 11% over traditional machine learning techniques. This framework offers an effective method for rapid and consistent diagnosis, designed primarily to assist clinicians in accurately identifying the virus.
Accurate and timely diagnosis of viral infections is vital for halting disease spread and reducing the burden on healthcare systems. However, artificial intelligence tools often struggle when clinical data is scarce. Data-augmentation techniques overcome this barrier by enabling deep learning systems to train effectively on limited data, helping clinicians identify infections more rapidly and consistently during critical healthcare crises.
The models are intended for integration into clinical diagnostic decision-support software used by healthcare workers to detect COVID-19 infections rapidly. Based on the abstract, the work is at an applied and tested experimental stage, having demonstrated improved diagnostic metrics and testing times in computational evaluations. Further development would require real-world clinical validation before commercial deployment in hospital or diagnostic laboratory environments.
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This generation faces existential threats because of the global assault of the novel Corona virus 2019 (i.e., COVID-19). With more than thirteen million infected and nearly 600000 fatalities in 188 countries/regions, COVID-19 is the worst calamity since the World War II. These misfortunes are traced to various reasons, including late detection of latent or asymptomatic carriers, migration, and inadequate isolation of infected people. This makes detection, containment, and mitigation global priorities to contain exposure via quarantine, lockdowns, work/stay at home, and social distancing that are focused on "flattening the curve". While medical and healthcare givers are at the frontline in the battle against COVID-19, it is a crusade for all of humanity. Meanwhile, machine and deep learning models have been revolutionary across numerous domains and applications whose potency have been exploited to birth numerous state-of-the-art technologies utilised in disease detection, diagnoses, and treatment. Despite these potentials, machine and, particularly, deep learning models are data sensitive, because their effectiveness depends on availability and reliability of data. The unavailability of such data hinders efforts of engineers and computer scientists to fully contribute to the ongoing assault against COVID-19. Faced with a calamity on one side and absence of reliable data on the other, this study presents two data-augmentation models to enhance learnability of the Convolutional Neural Network (CNN) and the Convolutional Long Short-Term Memory (ConvLSTM)-based deep learning models (DADLMs) and, by doing so, boost the accuracy of COVID-19 detection. Experimental results reveal improvement in terms of accuracy of detection, logarithmic loss, and testing time relative to DLMs devoid of such data augmentation. Furthermore, average increases of 4% to 11% in COVID-19 detection accuracy are reported in favour of the proposed data-augmented deep learning models relative to the machine learning techniques. Therefore, the proposed algorithm is effective in performing a rapid and consistent Corona virus diagnosis that is primarily aimed at assisting clinicians in making accurate identification of the virus.
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DOI: 10.3390/v12070769
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