World Health Organization (WHO) gave the name “Corona Disease of 2019 (COVID-19)”, caused by the unique form of virus structure as SARS-COV-2. Medical authority registered the first case in Wuhan, China and afterwards it exponentially spreads among people globally. Till date, the WHO, UN and other worldwide organizations reported many deaths (in thousands and still counting) due to the unavailability of any cure as well as late detection. In addition, hospitals and government suffer from deficiency of general resources such as PPE kit, ventilators, masks, gloves, general medicines etc. that makes the condition more severe. Therefore, it becomes essential to implement an automatic detection model by using advanced image processing techniques, which deliver the instant diagnostic report in order to help in prevention from COVID-19 dispersion. This qualitative assessment offers the significant image processing techniques to detect the early symptoms of COVID-19 from X-ray reports or radiographs. For instance, Inception-ResNetV2, InceptionV3 and ResNet50 are some forms of Convolutional Neural Network schemes that have the potential to detect the COVID-19. This assessment report presents the other effective image processing schemes that can utilize to detect the spreading of virus in human body accurately within short duration. Such models needed the highest classification performance with authenticate datasets of patients. The best feature of advanced image processing is that it is highly compatible with other progressive techniques such as Machine Learning, Artificial Intelligence, Data Analytics, and Robotics etc. All such techniques are highly dependable on image processing procedures such as image detection, segmentation, and feature extraction for the analysis of images. Each detection process initiated with image acquisition step such as X-ray, Computed Tomography (CT) images etc. Therefore, each technology needs the help of advanced image processing schemes for further assessment of disease. Early phase detection is the most crucial stage in disease prevention. This article depicts the potential of the algorithms used in feature extraction such as Discrete Wavelet Transform (DWT), Grey-Level Size Zone Matrix (GLSZM), Grey Level Run Length Matrix (GLRLM), Local Direction Pattern (LDP), Grey Level Co-occurrence Matrix (GLCM), Support Vector Machines (SVM), etc. Some of the performance metrics of COVID-19 detection via image processing techniques are F-score, precision, accuracy, specificity and sensitivity. The existing work illustrates the highest classification accuracy of GLSZM feature extraction method among the others. Such image processing techniques can efficiently detect the minute spalls or cracks occurred in respiratory organs due to COVID-19.This study highlights the cost-effective methodologies to practice the medical treatments in spite of the others labor and time-consuming procedures. Image processing algorithms can extract the significant features from digital images due to the presence of projection integrals and steerable filters. Therefore, this assessment presents the correlation between imaging manifestations and medical practices on COVID-19.

Keywords: Advanced Image Processing Techniques, CT Scan, X-ray, CNN, Image Classification.