  Given that lung cancer is one of the deadliest diseases, and many die from
the disease every year, early detection and diagnosis of this disease are
valuable, preventing cancer from growing and spreading. So if cancer is
diagnosed in the early stage, the patient's life will be saved. However, the
current pulmonary disease diagnosis is made by human resources, which is
time-consuming and requires a specialist in this field. Also, there is a high
level of errors in human diagnosis. Our goal is to develop a system that can
detect and classify lung lesions with high accuracy and segment them in CT-scan
images. In the proposed method, first, features are extracted automatically
from the CT-scan image; then, the extracted features are classified by Ensemble
Gradient Boosting methods. Finally, if there is a lesion in the CT-scan image,
using a hybrid method based on [1], including Fuzzy Clustering and Level Set,
the lesion is segmented. We collected a dataset, including CT-scan images of
pulmonary lesions. The target community was the patients in Mashhad. The
collected samples were then tagged by a specialist. We used this dataset for
training and testing our models. Finally, we were able to achieve an accuracy
of 96% for this dataset. This system can help physicians to diagnose pulmonary
lesions and prevent possible mistakes.
