چكيده به لاتين
Lung cancer that caused by abnormal growth and proliferation of cells in the lung tissue, has the highest mortality rate among all types of cancers. Tumors that grow in the lungs are called nodules that can be benign (cancerous) or malignant (cancerous). Symptoms of lung cancer usually do not appear until the cancer progresses. Therefore, early diagnosis of lung cancer is vital in the healthcare and treatment fields. Because with the early diagnosis and treatment of the disease, the survival of the patient with lung cancer can be increased. One of the ways of early detection of lung cancer is CT scan imaging, which as a non-invasive and painless method, helps radiologists in the diagnosis of the type of nodule and helps doctors to choose the appropriate treatment. Diagnosis of lung nodules from CT scan images by radiologists may be an error. A computer-aided diagnostic system, as a decision support system, can help radiologists to diagnosis and classify lung nodules. To create a computer-aided diagnostic system for nodule classification, proper features should be extracted from the images. Deep learning is one of the most important methods of deep learning in the field of medical image analysis. The convolutional neural network has become one of the popular methods in the field of medicine, among the deep learning methods, due to the preservation of the spatial structure of the image and the ability for automatically extract high-level features. The network efficiency largely depends on fine-tuning of its hyper-parameters. In this thesis, a convolutional neural network is proposed for the classification of lung nodules from CT scan images. The accuracy and f1-score is considered for evaluation of proposed network performance as a response variable. After finding the appropriate regression for them using the Design Of Experiment methods and considering the limitations of the hyper-parameters, a mixed integer nonlinear programming problem is obtained which, with a solution of 92/1% for accuracy and 88 % for F1-score is obtained.
