![normal chest xray image normal chest xray image](https://handheldultrasound.dev.gehealthcare.com/en-sg/wp-content/uploads/sites/4/2021/02/pleural_effusion-768x180.jpg)
![normal chest xray image normal chest xray image](https://mma.prnewswire.com/media/1576401/Lunit_CI_Logo.jpg)
Despite the mortality rates of these epidemics being much higher than that of COVID-19 (10% for SARS and 30-35% for MERS), the cumulative number of deaths for the latter has surpassed that of both the epidemics combined by many folds. Among them, two had caused epidemics in the last two decades named SARS-CoV and MERS-CoV. Until SARS-CoV-2 surfaced, six types of coronaviruses were known to be able to harm humans by mainly targeting the respiratory system. The virus responsible for the disease, named SARS-CoV-2, belongs to a family of coronaviruses that are zoonotic in nature. Therefore, our proposed approach can be adapted as a reliable method for faster and accurate COVID-19 affected case detection.ĬOVID-19, the pandemic that has brought the world to a halt, was reported in Wuhan, China in the December of 2019 for the first time, when patients with cases of unidentified pneumonia emerged. Most importantly, the model has shown a significantly good performance over the current research-based methods in detecting the COVID-19 cases in the test dataset (Precision = 1.00, Recall = 1.00, F1-score = 1.00 and Specificity = 1.00). The implemented neural network demonstrates significant performance in classifying the cases with an overall accuracy of 96%. Our dataset consists of 2905 chest X-ray images of three categories: COVID-19 affected (219 cases), Viral Pneumonia affected (1345 cases), and Normal Chest X-rays (1341 cases). In this paper, implementation of a deep transfer learning-based framework using a pre-trained network (ResNet-50) for detecting COVID-19 from the chest X-rays was done. A significant step of COVID-19 affected patient’s treatment is the faster and accurate detection of the disease which is the motivation of this study. Adv Health Sci Educ Theory Pract 2017 22 (3):765–87.World economy as well as public health have been facing a devastating effect caused by the disease termed as Coronavirus (COVID-19). How visual search relates to visual diagnostic performance: a narrative systematic review of eye‐tracking research in radiology. Van der Gijp A, Ravesloot CJ, Jarodzka H, van der Schaaf MF, van der Schaaf IC, van Schaik JPJ, Ten Cate TJ. What we do and do not know about teaching medical image interpretation. Kok EM, van Geel K, van Merriënboer JJG, Robben SGF. The challenges of studying visual expertise in medical image diagnosis. Gegenfurtner A, Kok E, van Geel K, de Bruin A, Jarodzka H, Szulewski A, van Merriënboer JJG. Prevalence of abnormal cases in an image bank affects the learning of radiograph interpretation. Pusic MV, Andrews JS, Kessler DO, Teng DC, Pecaric MR, Ruzal‐Shapiro C, Boutis K. The new era of medical imaging–progress and pitfalls. Medical Education published by Association for the Study of Medical Education and John Wiley & Sons Ltd. Deductive approaches are therefore advised for the training of advanced learners. An inductive approach did not lead to higher diagnostic performance, possibly because participants might already have relevant prior knowledge. Furthermore, the deductive conditions unexpectedly scored higher on specificity when participants took less time per case. This trade-off should be an important consideration for the alignment of training with future practice. The proportion of normal images impacted the sensitivity-specificity trade-off.
![normal chest xray image normal chest xray image](https://guiasteam.com/wp-content/uploads/2020/12/1607529086_510_Monster-Sanctuary-MAP-Monster-Location-Chest-Location-Guide.png)
They had similar test sensitivity, but scored lower on test specificity. Those who participated in inductive conditions took less time per practice case but more per test case. The conditions with 30% of normal images scored higher on sensitivity but the conditions with 70% of normal images scored higher on specificity, indicating a sensitivity and specificity trade-off.
![normal chest xray image normal chest xray image](https://thumbs.dreamstime.com/z/normal-chest-ray-human-patient-91619153.jpg)
After training, students performed a test (60% normal) and sensitivity (% of correctly identified abnormal radiographs), specificity (% of correctly identified normal radiographs), diagnostic performance (% of correct diagnoses) and case duration were measured. Third-year medical students (n = 103) learned radiograph interpretation by practising cases with, respectively, 30% or 70% normal radiographs prior to expert instruction (practice-first order) or after expert instruction (instruction-first order). It is hypothesised that manipulation of the proportion of normal images will lead to a sensitivity-specificity trade-off and that students in practice-first (inductive) conditons need more time per practice case but will correctly identify more test cases. This study investigates the effects of the proportion of normal images and practice-instruction order on learning to interpret medical images. Furthermore, instructional sequences that let students practice prior to expert instruction (inductive) may lead to improved performance compared with methods that give students expert instruction before practice (deductive). Medical image perception training generally focuses on abnormalities, whereas normal images are more prevalent in medical practice.