Nevin Aydin, Özer Çelik, Ahmet Faruk Aslan, Alper Odabaş, Emine Dündar and Meryem Cansu Şahin* Pages 1 - 5 ( 5 )
Background: In every year, lung cancer is an important cause of deaths in the world. Early detection of lung cancer is important for treatment, and non-invasive rapid methods are needed 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).
Method: A total of 301 patients with diagnosed with lung carcinoma pathologies in our hospital were included in the study. In the thorax computed tomography (CT) performed for diagnostic purposes prior to treatment. After tagging the section images, tumor detection, small-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.
Result: In total, 301 lung carcinoma images were used to detect tumors, and the model obtained with the deep CNN system had 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 for 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 success of the deep learning methods, they should be integrated into radiology to diagnose lung carcinoma.
Lung Cancer, Adenocarcinoma, Deep Learning, Convolutional Neural Network, Algorithm, Computed Tomography.
Department of Radiology, Faculty of Medicine, Eskisehir Osmangazi University, Eskisehir, , Department of Mathematics and Computer, Faculty of Science and Letters, Eskisehir Osmangazi University, Eskisehir, , Department of Mathematics and Computer, Faculty of Science and Letters, Eskisehir Osmangazi University, Eskisehir, , Department of Mathematics and Computer, Faculty of Science and Letters, Eskisehir Osmangazi University, Eskisehir, , Department of Medical Pathology, Faculty of Medicine, Eskisehir Osmangazi University, Eskisehir, , Kutahya Health Sciences University Research and Training Center, Kutahya