Infection Severity Detection of CoVID19 from X-Rays and CT Scans Using Artificial Intelligence
Keywords:
Pre-Trained Architectures, VGG, ResNet50V2, InceptionResNetV2, DenseNet, Xception, MobileNet, NASNet, COVID19, SARS-CoV2, Neural Networks, Computer Vision, PandemicAbstract
December 2019, marked with a widespread infection due to a new matured member of SARs Virus named as SARS-CoV2 (Novel Corona Virus-2019) infecting more than 20 lakhs people across the globe. This effect made the World Health Organization to declare COVID-19 (Corona Virus Disease, 2019) as a pandemic situation and called a worldwide lockdown to dampen and flatten the infectious curve and diminish the infection growth. With Limited number of COVID-19 test kits in hospitals and the increasing daily cases has asked for an immediate measure for the development towards the Automatic COVID-19 Detection and Alternative Diagnosis Systems (ACD-ADS). This research presents a two-staged DenseNet architecture to diagnose the COVID19 infections from X-rays and CT-scans images to decrease the turnaround time of the doctors and check more patients during that point of time. This research work talks about the end to end solution for the diagnosis to extract and mark the most infectious regions on the imaging pictures to help the doctors and medical practitioners in this pandemic situation. The system achieved an accuracy of 99% and specificity of 94.1% using the DenseNet network on the X-rays images and an accuracy of 87% and specificity of 86.5% for the CT Scans in the Validation Sets. In a sample of 22 images for the CT-Scans of the reported patients having the COVID-19 infections in a real-time analysis, the model performed with detecting correctly for all the 22 patients. Any model can never replace a doctor nor can decide like a doctor who takes many other factors into the account that impacts a decision at a particular point of time. Hence, I propose a network called Automatic Diagnostic Medical Analysis for the COVID-19 Detection System (ADMCDS) that takes the images and tries to find the infectious regions to help the doctor better identifying the diseased part if any.
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