Accurate Identification of Tomograms of Lung Nodules Using CNN: Influence of the Optimizer, Preprocessing and Segmentation

The diagnosis of pulmonary nodules plays an important role in the treatment of lung cancer, thus improving the diagnosis is the primary concern. This article shows a comparison of the results in the identification of computed tomography scans with pulmonary nodules, through the use of different optimizers (Adam and Nadam); the effect of the use of pre-processing and segmentation techniques using CNNs is also thoroughly explored. The dataset employed was Lung TIME which is publicly available. When no preprocessing or segmentation was applied, training accuracy above 90.24% and test accuracy above 86.8% were obtained. In contrast, when segmentation was applied without preprocessing, a training accuracy above 97.19% and test accuracy above 95.07% were reached. On the other hand, when preprocessing and segmentation was applied, a training accuracy above 96.41% and test accuracy above 94.71% were achieved. On average, the Adam optimizer scored a training accuracy of 96.17% and a test accuracy of 95.23%. Whereas, the Nadam optimizer obtained 96.25% and 95.2%, respectively. It is concluded that CNN has a good performance even when working with images with noise. The performance of the network was similar when working with preprocessing and segmentation than when using only segmentation. Also, it can be inferred that, the application of preprocessing and segmentation is an excellent option when it is required to improve accuracy in CNNs.