A hierarchical GAN method with ensemble CNN for accurate nodule detection

Lung cancer can evolve into one of the deadliest diseases whose early detection is one of the major survival factors. However, early detection is a challenging task due to the unclear structure, shape, and the size of the nodule. Hence, radiologists need automated tools to make accurate decisions.

This paper develops a new approach based on generative adversarial network (GAN) architecture for nodule detection to propose a two-step GAN model containing lung segmentation and nodule localization. The first generator comprises a U-net network, while the second utilizes a mask R-CNN. The task of lung segmentation involves a two-class classification of the pixels in each image, categorizing lung pixels in one class and the rest in the other. The classifier becomes imbalanced due to numerous non-lung pixels, decreasing the model performance. This problem is resolved by using the focal loss function for training the generator. Moreover, a new loss function is developed as the nodule localization generator to enhance the diagnosis quality. Discriminator nets are implemented in GANs as an ensemble of convolutional neural networks (ECNNs), using multiple CNNs and connecting their outputs to make a final decision.

Several experiments are designed to assess the model on the well-known LUNA dataset. The experiments indicate that the proposed model can reduce the error of the state-of-the-art models on the IoU criterion by about 35 and 16% for lung segmentation and nodule localization, respectively.

Unlike recent studies, the proposed method considers two loss functions for generators, further promoting the goal achievements. Moreover, the network of discriminators is regarded as ECNNs, generating rich features for decisions.