Deep Learning in Distinguishing Pulmonary Nodules as Benign and Malignant

Abstract

Background: Due to the high mortality of lung cancer, the aim was to find convolutional neural network models that can distinguish benign and malignant cases with high accuracy, which can help in early diagnosis with diagnostic imaging. Methods: Patients who underwent tomography in our clinic and who were found to have lung nodules were retrospectively screened between January 2015 and December 2020. The patients were divided into two groups: benign (n=68; 38 males, 30 females; mean age: 59 +/- 12.2 years; range, 27 to 81 years) and malignant (n=29; 19 males, 10 females; mean age: 65 +/- 10.4 years; range, 43 to 88 years). In addition, a control group (n=67; 38 males, 29 females; mean age: 56.9 +/- 14.1 years; range, 26 to 81 years) consisting of healthy patients with no pathology in their sections was formed. Deep neural networks were trained with 80% of the three-class dataset we created and tested with 20% of the data. After the training of deep neural networks, feature extraction was done for these networks. The features extracted from the dataset were classified by machine learning algorithms. Performance results were obtained using confusion matrix analysis. Results: After training deep neural networks, the highest accuracy rate of 80% was achieved with the AlexNET model among the models used. In the second stage results, obtained after feature extraction and using the classifier, the highest accuracy rate was achieved with the support vector machine classifier in the VGG19 model with 93.5%. In addition, increases in accuracy were noted in all models with the use of the support vector machine classifier. Conclusion: Differentiation of benign and malignant lung nodules using deep learning models and feature extraction will provide important advantages for early diagnosis in radiology practice. The results obtained in our study support this view.