COVID-19 Diagnosis and Classification Using Radiological Imaging and Deep Learning Techniques: A Comparative Study

Abstract

In December 2019, the novel coronavirus disease 2019 (COVID-19) appeared. Being highly contagious and with no effective treatment available, the only solution was to detect and isolate infected patients to further break the chain of infection. The shortage of test kits and other drawbacks of lab tests motivated researchers to build an automated diagnosis system using chest X-rays and CT scanning. The reviewed works in this study use AI coupled with the radiological image processing of raw chest X-rays and CT images to train various CNN models. They use transfer learning and numerous types of binary and multi-class classifications. The models are trained and validated on several datasets, the attributes of which are also discussed. The obtained results of various algorithms are later compared using performance metrics such as accuracy, F1 score, and AUC. Major challenges faced in this research domain are the limited availability of COVID image data and the high accuracy of the prediction of the severity of patients using deep learning compared to well-known methods of COVID-19 detection such as PCR tests. These automated detection systems using CXR technology are reliable enough to help radiologists in the initial screening and in the immediate diagnosis of infected individuals. They are preferred because of their low cost, availability, and fast results.

Deep Learning Algorithm for Real-time Disease Detection and Classification in Radiological Images with Enhanced Diagnostic Accuracy

“…Nevertheless, potential limitations encompass the necessity for more extensive coverage of recent developments and practical considerations for clinical implementation to ensure the relevance and applicability of the review findings. In a comparative study, various approaches for diagnosing COVID-19 using radiological imaging and deep learning techniques are evaluated [13]. While furnishing valuable insights into the performance of these approaches, variations in dataset characteristics and model architectures may limit the generalizability of the findings across diverse healthcare settings.…”

Artificial neural network based prediction of the lung tissue involvement as an independent in‐hospital mortality and mechanical ventilation risk factor in COVID‐19

“…The analysis of CT images requires expert radiologic assessment and time, both of which may be of limited availability during a pandemic. Furthermore, the extent of lung tissue involvement may vary because of human error, which reduces the accuracy and usefulness of CT radioimaging data for prediction models 13–15 . Lastly, radiological data alone do not account for the remaining clinically important variables, including laboratory parameters, clinical patient data, or comorbidities.…”

Deep learning for COVID‐19 contamination analysis and prediction using ECG images on Raspberry Pi 4

“…They provide objective measurements that can be easily quantified and analyzed using AI algorithms. This can improve the accuracy and efficiency of COVID‐19 diagnosis 20–24 . Deep learning algorithms require a large amount of high‐quality labeled data for training.…”

“…This can improve the accuracy and efficiency of COVID-19 diagnosis. [20][21][22][23][24] Deep learning algorithms require a large amount of highquality labeled data for training. However, in the case of COVID-19 diagnosis, such data is limited, which can hinder the development and evaluation of effective deeplearning models.…”