Role of Segmentation in Lung Nodules CT Scan Images for High Performance: Case of Recent Findings

Abstract

Early stage of lung cancer detection is being very vital in today’s scenario. Several promising technological diagnostic automated tools developed and are used to predict the patient's survival using intelligent lung nodules analysis in Computed Tomography (CT) images.  Recently, the advancement of human health diagnostic technologies, Computer-Aided Detection Systems (CADe) are developed diurnal rhythms to provide higher accuracy and better performance rate. In this research work, we focuses on a documenting the automatic segmentation of lung cancer nodules by various methods developed in recent years of the timeline with respect to the realization. The proposed work is criterion based objectives to establish the performance measures and to propose new solution for Lung nodules segmentation and detection on CT Scanned digital images, which is the fundamental and epochal measure to attain the high level of performance. In analyzing each of these technical execution on detection on lung nodule, and experimented to 4 different kinds of lung nodules CT scanned digital images. The phenomenon show segmentation methods involving convolution neural networks were better when compared to other existing methods. U-Net method gave improved accuracy. Dice score 0.981±0.009 and sensitivity 0.994±0.002 are maximum by using the method Dense R2UNet.  F1-score 97% is best achieved by Cascade Convolutional Network. Recall rate is achieved to the maximum 99.1% through Improved 3D-UNet Neural Network. Precision 0.982±0.009 and accuracy 0.988±0.018 are best attained by Dense R2UNet. In the case, semi-transparent nodules segmentation, Watershed method found to be highly appropriate select. Watershed arse nodules segment correspond well to vessels and semi-transparent nodules, and exhibit low sensitivity in solitary or lone nodules. The higher rate of efficient segmentation is directly proportional to higher rate of performance.