Artificial Intelligence-Based Mining of Benign and Malignant Characteristics of Pulmonary Ground- Glass Nodules

Abstract

Deep learning-based Artificial Intelligence (AI) with medical imaging cooperation has led to a significant increase in the detection rate of early lung cancer, but the identification of its benign and malignant nature remains difficult. This chapter describes the effect of AI on CT values, maximum surface area, volume, 3D longitudinal diameter, solid occupancy, multiplication time, and also ground glass pulmonary nodule. We believe that AI will be able to provide a variety of features for the analysis of the characteristics of the world's largest and most complex computer systems. It helps radiologists to determine the benign and malignant nature of pulmonary ground-glass nodules and improves the accuracy and quality of diagnosis. Besides, patient’s survival rate and quality of life can be improved as well.

Keywords: Artificial intelligence, Deep learning, Malignant characteristics, Pulmonary ground-glass nodules, Quality of life.

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