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Given that lung cancer is one of the deadliest illnesses, earlyidentification and diagnosis are critical to preserving a patient's life.However, lung illness diagnosis is time-intensive and requires the expertise ofa pulmonary disease specialist, subject to a significant rate of inaccuracy.Our objective is to design a system capable of accurately detecting andclassifying lung lesions and segmenting them in CT-scan images. The suggestedtechnique extracts features automatically from the CT-scan image and thenclassifies them using Ensemble Gradient Boosting methods. Finally, if a lesionis detected in the CT-scan image, it is segmented using a hybrid approach basedon Fuzzy Clustering and Level Set. To train and test our models we gathered adataset that included CT images of patients residing in Mashhad, Iran. Finally,the results indicate 96% accuracy within this dataset. This approach may assistclinicians in diagnosing lung abnormalities and avoiding potential errors.

Classification and Segmentation of Pulmonary Lesions in CT Images Using  a Combined VGG-XGBoost Method, and an Integrated Fuzzy Clustering-Level Set  Technique