چكيده به لاتين
Lung cancer is an aggressive disease resulting in more than one million deaths per year worldwide. Sometimes there is a suspicious tissue in the lungs, which is referred to as a lung nodule. Early stage lung cancer generally manifests in the form of pulmonary nodules and to determine if someone will develop lung cancer, we have to look for early stages of malignant pulmonary nodules. Pulmonary nodules have different types and forms, and even all of them are not malignant or cancerous. This great difference in the shape of nodules, as well as their small size, makes nodule detection more difficult. In this thesis, we have tried to study the lung cancer screening, by using modern methods, in particular machine learning and deep learning algorithms. During this research, an automatic diagnostic system for the diagnosis of pulmonary nodule has been designed and implemented, and details and explanations related to it are presented in this thesis. The encoder part of the proposed architecture, is NASNet-mobile and we designed the decoder part, with a lot of investigation and trial and error. In this research, the segmentation method is used for detecting lung nodules. We achieved for dice coefficient in segmentation and for sensitivity measure.
Keywords: Lung Cancer, Pulmonary Nodule, Deep Learning, Machine Learning
