Publication

Abstract : The pulmonary nodules relate to a variety of lung irregularities that can be found in the early diagnosis of pulmonary patients. Radiologists can diagnose lung nodules by analyzing pulmonary images. The radiologists may be assisted by automatic sensing devices that identify nodules of various sizes within lung images. The identification and categorization of lung nodules on various images, such as CT images, electron microscopy, and Histopathology images, have attracted substantial mathematical, statistical, and observational study work during the past 50 years. Various methods appear to be promising suitable decision analysis systems to adequately handle the core problems in lung nodules diagnosis, such as extraction of features, nodule identification, true-negative reduction, and cancerous-noncancerous differentiation, as noted in a current remarkable and considerable improvements in machine learning for pulmonary nodules they attained across both research and commercial. The major goal of this study is to offer a complete state-of-the-art analysis of various methodologies for finding a better method for the classification of lung CT images in malignant and benign classes. An analysis of the methods of automatic detection of lung nodules is given in this paper. It implements a common framework that can be used to define and reflect current methods for the identification of lung nodules.