Lung Nodule Segmentation Using Cat Swarm Optimization Based Recurrent Neural Network.

Nodule segmentation in lung computed tomography (CT) images is a significant part of the detection and diagnosis of lung cancer. Automatic analysis of lung CT images is necessary to calculate lung nodule characteristics for recognizing malignancy. In recent years, deep learning and neural networks have been used in medical applications. Deep learning utilizes neural networks to train huge amounts of information which effectively learns the nodule features in lower to higher grades for segmenting and predicting the medical images. In this paper, the proposed cat swarm optimization (CSO) based recurrent neural network (RNN) is utilized for lung nodule segmentation. The proposed model is estimated on the freely accessible lung image database consortium and image database resource initiative (LIDC-IDRI) dataset. The proposed model is segmented by using the markov random field (MRF) based firefly algorithm (FA) and cuckoo search algorithm (CSA). The result shows that the proposed CSO based RNN model delivers performance metrics like dice coefficient (DC) loss and accuracy values of about 96.28% and 90.28% respectively, which ensures accurate nodule segmentation in lung CT images. [ABSTRACT FROM AUTHOR]