Background: Every year, lung cancer contributes to a high percentage deaths in the
world. Early detection of lung cancer is important for its effective treatment, and non-invasive
rapid methods are usually used for diagnosis.
Introduction: In this study, we aimed to detect lung cancer using deep learning methods and determine
the contribution of deep learning to the classification of lung carcinoma using a convolutional
neural network (CNN).
Methods: A total of 301 patients diagnosed with lung carcinoma pathologies in our hospital were
included in the study. In the thorax, Computed Tomography (CT) was performed for diagnostic purposes
prior to the treatment. After tagging the section images, tumor detection, small and non-small
cell lung carcinoma differentiation, adenocarcinoma-squamous cell lung carcinoma differentiation,
and adenocarcinoma-squamous cell-small cell lung carcinoma differentiation were sequentially
performed using deep CNN methods.
Results: In total, 301 lung carcinoma images were used to detect tumors, and the model obtained
with the deep CNN system exhibited 0.93 sensitivity, 0.82 precision, and 0.87 F1 score in detecting
lung carcinoma. In the differentiation of small cell-non-small cell lung carcinoma, the sensitivity,
precision and F1 score of the CNN model at the test stage were 0.92, 0.65, and 0.76, respectively.
In the adenocarcinoma-squamous cancer differentiation, the sensitivity, precision, and F1 score
were 0.95, 0.80, and 0.86, respectively. The patients were finally grouped as small cell lung carcinoma,
adenocarcinoma, and squamous cell lung carcinoma, and the CNN model was used to determine
whether it could differentiate these groups. The sensitivity, specificity, and F1 score of this
model were 0.90, 0.44, and 0.59, respectively, in this differentiation.
Conclusion: In this study, we successfully detected tumors and differentiated between adenocarcinoma-
squamous cell carcinoma groups with the deep learning method using the CNN model. Due
to their non-invasive nature and the success of the deep learning methods, they should be integrated
into radiology to diagnose lung carcinoma.