摘要

COVID-19 IS A RESPIRATORY DISORDER CAUSED BY CORONAVIRUS AND SARS (SARS-COV2). WHO DECLARED COVID-19 A GLOBAL PANDEMIC IN MARCH 2020 AND SEVERAL NATIONS’ HEALTHCARE SYSTEMS WERE ON THE VERGE OF COLLAPSING. WITH THAT, BECAME CRUCIAL TO SCREEN COVID-19-POSITIVE PATIENTS TO MAXIMIZE LIMITED RESOURCES. NAATS AND ANTIGEN TESTS ARE UTILIZED TO DIAGNOSE COVID-19 INFECTIONS. NAATS RELIABLY DETECT SARS-COV-2 AND SELDOM PRODUCE FALSE-NEGATIVE RESULTS. BECAUSE OF ITS SPECIFICITY AND SENSITIVITY, RT-PCR CAN BE CONSIDERED THE GOLD STANDARD FOR COVID-19 DIAGNOSIS. THIS TEST’S COMPLEX GEAR IS PRICEY AND TIME-CONSUMING, USING SKILLED SPECIALISTS TO COLLECT THROAT OR NASAL MUCUS SAMPLES. THESE TESTS REQUIRE LABORATORY FACILITIES AND A MACHINE FOR DETECTION AND ANALYSIS. DEEP LEARNING NETWORKS HAVE BEEN USED FOR FEATURE EXTRACTION AND CLASSIFICATION OF CHEST CT-SCAN IMAGES AND AS AN INNOVATIVE DETECTION APPROACH IN CLINICAL PRACTICE. BECAUSE OF COVID-19 CT SCANS’ MEDICAL CHARACTERISTICS, THE LESIONS ARE WIDELY SPREAD AND DISPLAY A RANGE OF LOCAL ASPECTS. USING DEEP LEARNING TO DIAGNOSE DIRECTLY IS DIFFICULT. IN COVID-19, A TRANSFORMER AND CONVOLUTIONAL NEURAL NETWORK MODULE ARE PRESENTED TO EXTRACT LOCAL AND GLOBAL INFORMATION FROM CT IMAGES. THIS CHAPTER EXPLAINS TRANSFER LEARNING, CONSIDERING VGG-16 NETWORK, IN CT EXAMINATIONS AND COMPARES CONVOLUTIONAL NETWORKS WITH VISION TRANSFORMERS (VIT). VIT USAGE INCREASED VGG-16 NETWORK F1-SCORE TO 0.94.