LNCDS: A 2D-3D cascaded CNN approach for lung nodule classification, detection and segmentation

Abstract

The early detection of lung cancer is attained with the detection of initial stage nodules (3−30mm) which can exorbitantly increase the 5-year survival rate of lung cancer patients. Nodules are very small size circumscribed structures in the lungs and are difficult to detect due to their size. The identification of nodule is also more challenging due to similarly resembling structures like non-nodules that contains features which could make it identifiable as a nodule. Therefore, to deal with these challenging issues, we proposed a novel approach for segmentation, classification and detection of lung nodules from CT scan images. In our proposed method we involve a maximum intensity projection technique as a part of image preprocessing method. We demonstrated our experimentation and proposed SquExUNet segmentation model and 3D-NodNet classification model on publicly available Lung Image Database Consortium – Image Database Resource Initiative (LIDC), LNDb Challenge Dataset and purely independent Indian Lung CT Image Database (ILCID) clinical dataset. We proposed a 2D-3D cascaded CNN strategy for detection of nodule that yields the accurately segmented and classified nodule. Results obtained with proposed method indicates that we have successfully detected and segmented the lung nodules effectively, compared to existing lung nodule detection and segmentation algorithms. We achieved a Dice-Coefficient metrics of 0.80 for segmentation of nodule and 90.01% Sensitivity for nodule detection.