COVID-19 CLASSIFICATION USING CT SCANS WITH CONVOLUTIONAL NEURAL NETWORKS

Abstract

EVEN WITH MORE THAN 12 BILLION VACCINE DOSES ADMINISTERED GLOBALLY, THE COVID-19 PANDEMIC HAS CAUSED SEVERAL GLOBAL ECONOMIC, SOCIAL, ENVIRONMENTAL, AND HEALTHCARE IMPACTS. COMPUTER AIDED DIAGNOSTIC (CAD) SYSTEMS CAN SERVE AS A COMPLEMENTARY METHOD TO AID DOCTORS IN IDENTIFYING REGIONS OF INTEREST IN IMAGES AND HELP DETECT DISEASES. IN ADDITION, THESE SYSTEMS CAN HELP DOCTORS ANALYZE THE STATUS OF THE DISEASE AND CHECK FOR THEIR PROGRESS OR REGRESSION. TO ANALYZE THE VIABILITY OF USING CNNS FOR DIFFERENTIATING COVID-19 CT POSITIVE IMAGES FROM COVID-19 CT NEGATIVE IMAGES, WE USED A DATASET COLLECTED BY UNION HOSPITAL (HUST-UH) AND LIYUAN HOSPITAL (HUST-LH) AND MADE AVAILABLE AT THE KAGGLE PLATFORM. THE MAIN OBJECTIVE OF THIS CHAPTER IS TO PRESENT RESULTS FROM APPLYING TWO STATE-OF-THE-ART CNNS ON A COVID-19 CT SCAN IMAGES DATABASE TO EVALUATE THE POSSIBILITY OF DIFFERENTIATING IMAGES WITH IMAGING FEATURES ASSOCIATED WITH COVID-19 PNEUMONIA FROM IMAGES WITH IMAGING FEATURES IRRELEVANT TO COVID-19 PNEUMONIA. TWO PRE-TRAINED NEURAL NETWORKS, RESNET50 AND MOBILENET, WERE FINE-TUNED FOR THE DATASETS UNDER ANALYSIS. BOTH CNNS OBTAINED PROMISING RESULTS, WITH THE RESNET50 NETWORK ACHIEVING A PRECISION OF 0.97, A RECALL OF 0.96, AN F1-SCORE OF 0.96, AND 39 FALSE NEGATIVES. THE MOBILENET CLASSIFIER OBTAINED A PRECISION OF 0.94, A RECALL OF 0.94, AN F1-SCORE OF 0.94, AND A TOTAL OF 20 FALSE NEGATIVES.