摘要

THIS CHAPTER DESCRIBES AN AUTO-ML STRATEGY TO DETECT COVID ON CHEST X-RAYS UTILIZING TRANSFER LEARNING FEATURE EXTRACTION AND THE AUTOML TPOT FRAMEWORK IN ORDER TO IDENTIFY LUNG ILLNESSES (SUCH AS COVID OR PNEUMONIA). MOBILENET IS A LIGHTWEIGHT NETWORK THAT USES DEPTHWISE SEPARABLE CONVOLUTION TO DEEPEN THE NETWORK WHILE DECREASING PARAMETERS AND COMPUTATION. AUTOML IS A REVOLUTIONARY CONCEPT OF AUTOMATED MACHINE LEARNING (AML) THAT AUTOMATES THE PROCESS OF BUILDING AN ML PIPELINE INSIDE A CONSTRAINED COMPUTING FRAMEWORK. THE TERM “AUTOML” CAN MEAN A NUMBER OF DIFFERENT THINGS DEPENDING ON CONTEXT. AUTOML HAS RISEN TO PROMINENCE IN BOTH THE BUSINESS WORLD AND THE ACADEMIC COMMUNITY THANKS TO THE EVER-INCREASING CAPABILITIES OF MODERN COMPUTERS. PYTHON OPTIMISED ML PIPELINE (TPOT) IS A PYTHON-BASED ML TOOL THAT OPTIMIZES PIPELINE EFFICIENCY VIA GENETIC PROGRAMMING. WE USE TPOT BUILDS MODELS FOR EXTRACTED MOBILENET NETWORK FEATURES FROM COVID-19 IMAGE DATA. THE F1-SCORE OF 0.79 CLASSIFIES NORMAL, VIRAL PNEUMONIA, AND LUNG OPACITY.