Abstract

THE GOLD STANDARD TO DETECT SARS-COV-2 INFECTION CONSIDERS TESTING METHODS BASED ON POLYMERASE CHAIN REACTION (PCR). STILL, THE TIME NECESSARY TO CONFIRM PATIENT INFECTION CAN BE LENGTHY, AND THE PROCESS IS EXPENSIVE. IN PARALLEL, X-RAY AND CT SCANS PLAY AN IMPORTANT ROLE IN THE DIAGNOSIS AND TREATMENT PROCESSES. HENCE, A TRUSTED AUTOMATED TECHNIQUE FOR IDENTIFYING AND QUANTIFYING THE INFECTED LUNG REGIONS WOULD BE ADVANTAGEOUS. CHEST X-RAYS ARE TWO-DIMENSIONAL IMAGES OF THE PATIENT’S CHEST AND PROVIDE LUNG MORPHOLOGICAL INFORMATION AND OTHER CHARACTERISTICS, LIKE GROUND-GLASS OPACITIES (GGO), HORIZONTAL LINEAR OPACITIES, OR CONSOLIDATIONS, WHICH ARE TYPICAL CHARACTERISTICS OF PNEUMONIA CAUSED BY COVID-19. THIS CHAPTER PRESENTS AN AI-BASED SYSTEM USING MULTIPLE TRANSFER LEARNING MODELS FOR COVID-19 CLASSIFICATION USING CHEST X-RAYS. IN OUR EXPERIMENTAL DESIGN, ALL THE CLASSIFIERS DEMONSTRATED SATISFACTORY ACCURACY, PRECISION, RECALL, AND SPECIFICITY PERFORMANCE. ON THE ONE HAND, THE MOBILENET ARCHITECTURE OUTPERFORMED THE OTHER CNNS, ACHIEVING EXCELLENT RESULTS FOR THE EVALUATED METRICS. ON THE OTHER HAND, SQUEEZENET PRESENTED A REGULAR RESULT IN TERMS OF RECALL. IN MEDICAL DIAGNOSIS, FALSE NEGATIVES CAN BE PARTICULARLY HARMFUL BECAUSE A FALSE NEGATIVE CAN LEAD TO PATIENTS BEING INCORRECTLY DIAGNOSED AS HEALTHY. THESE RESULTS SUGGEST THAT OUR DEEP LEARNING CLASSIFIERS CAN ACCURATELY CLASSIFY X-RAY EXAMS AS NORMAL OR INDICATIVE OF COVID-19 WITH HIGH CONFIDENCE.