Radiologist-Level Two Novel and Robust Automated Computer-Aided Prediction Models for Early Detection of COVID-19 Infection from Chest X-ray Images

Abstract

COVID-19 is an ongoing pandemic that is widely spreading daily and reaches a significant community spread. X-ray images, computed tomography (CT) images and test kits (RT-PCR) are three easily available options for predicting this infection. Compared to the screening of COVID-19 infection from X-ray and CT images, the test kits(RT-PCR) available to diagnose COVID-19 face problems such as high analytical time, high false negative outcomes, poor sensitivity and specificity. Radiological signatures that X-rays can detect have been found in COVID-19 positive patients. Radiologists may examine these signatures, but it's a time-consuming and error-prone process (riddled with intra-observer variability). Thus, the chest X-ray analysis process needs to be automated, for which AI-driven tools have proven to be the best choice to increase accuracy and speed up analysis time, especially in the case of medical image analysis. We shortlisted four datasets and 20 CNN-based models to test and validate the best ones using 16 detailed experiments with fivefold cross-validation. The two proposed models, ensemble deep transfer learning CNN model and hybrid LSTMCNN, perform the best. The accuracy of ensemble CNN was up to 99.78% (96.51% average-wise), F1-score up to 0.9977 (0.9682 average-wise) and AUC up to 0.9978 (0.9583 average-wise). The accuracy of LSTMCNN was up to 98.66% (96.46% average-wise), F1-score up to 0.9974 (0.9668 average-wise) and AUC up to 0.9856 (0.9645 average-wise). These two best pre-trained transfer learning-based detection models can contribute clinically by offering the patients prediction correctly and rapidly.

A multimodal AI-based non-invasive COVID-19 grading framework powered by deep learning, manta ray, and fuzzy inference system from multimedia vital signs

“…Munish et al [38] introduced a deep learning binary classification framework to predict COVID-19 infection using CT and CXR images. They testes twenty models using four datasets that contain 2,450 CT and CXR images.…”

COVID-19 Diagnosis from Medical Images Using Transfer Learning

“…More recent studies were published about using prediction models for early detection of COVID-19 from X-ray images. In [ 20 ], four datasets were applied with fivefold cross-validation on CNN-based models. Out of 16 experiments, two proposed models, ensemble deep transfer learning CNN model and hybrid LSTMCNN, perform the best.…”

Employing a Convolutional Neural Network to Classify Medical Images: A Case Study

“…The idea of artificial neural networks is accepted from the concept of the work of the human brain in thinking and making decisions, where a multi-layer network structure is created by simulating the work of neurons in the brain [9] [10]. The convolutional neural network has recently achieved many triumphs in image analysis, especially chest X-ray images of COVID-19 patients [11][12][13][14][15]. Figure 1 illustrates how to analyse chest X-rays utilising deep learning techniques and determine whether an individual has COVID-19 or not.…”