LUNG SEGMENTATION OF CHEST X-RAYS USING UNET CONVOLUTIONAL NETWORKS

Abstract

THE GOLD STANDARD TO DETECT SARS-COV-2 INFECTION CONSIDER TESTING METHODS BASED ON POLYMERASE CHAIN REACTION (PCR). STILL, THE TIME NECESSARY TO CONFIRM PATIENT INFECTION CAN BE LENGTHY, AND THE PROCESS IS EXPENSIVE. ON THE OTHER HAND, X-RAY AND CT SCANS PLAY A VITAL ROLE IN THE AUXILIARY DIAGNOSIS PROCESS. 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 CHARACTERISTICS OF PNEUMONIA CAUSED BY COVID-19. BUT BEFORE THE COMPUTERIZED DIAGNOSTIC SUPPORT SYSTEM CAN CLASSIFY A MEDICAL IMAGE, A SEGMENTATION TASK SHOULD USUALLY BE PERFORMED TO IDENTIFY RELEVANT AREAS TO BE ANALYZED AND REDUCE THE RISK OF NOISE AND MISINTERPRETATION CAUSED BY OTHER STRUCTURES EVENTUALLY PRESENT IN THE IMAGES. THIS CHAPTER PRESENTS AN AI-BASED SYSTEM FOR LUNG SEGMENTATION IN X-RAY IMAGES USING A U-NET CNN MODEL. THE SYSTEM’S PERFORMANCE WAS EVALUATED USING METRICS SUCH AS CROSS-ENTROPY, DICE COEFFICIENT, AND MEAN IOU ON UNSEEN DATA. OUR STUDY DIVIDED THE DATA INTO TRAINING AND EVALUATION SETS USING AN 80/20 TRAIN-TEST SPLIT METHOD. THE TRAINING SET WAS USED TO TRAIN THE MODEL, AND THE EVALUATION TEST SET WAS USED TO EVALUATE THE PERFORMANCE OF THE TRAINED MODEL. THE RESULTS OF THE EVALUATION SHOWED THAT THE MODEL ACHIEVED A DICE SIMILARITY COEFFICIENT (DSC) OF 95%, CROSS ENTROPY OF 97%, AND MEAN IOU OF 86%.