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