A holistic deep learning approach for identification and classification of sub-solid lung nodules in computed tomographic scans. (June 2020)

• Record Type: Journal Article
• Authors: Savitha, G.; Jidesh, P.
• Is Part Of: Computers & electrical engineering. Volume 84(2020)
• Publication Date: 2020-06
• Subjects: Part-solid nodule -- Conditional random field -- Deep convolution neural network -- Computed tomography images; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2020.106626
• Languages: English
• ISSNs: 0045-7906

Abstract

Highlights: A holistic computer aided sub-solid nodule detection system is developed. The system is efficient in detecting part-solid nodules in lung CT images. The deep-learning model extracts features of the part-solid nodules and localize them. A further improvisation is done using Conditional Random Field algorithm (CRF). CRF technique reduces the false positives and increases the overall accuracy. Graphical abstract: Abstract: Prompt detection of malignant lung nodules significantly improves the chance of survivability of the affected patients. The lung nodules in their early stages appear as subsolid or part-solid nodules whose identification remains a challenging task. Many of the present lung nodule detection systems fail to identify the nodules in their early stages. Limitations in the feature extraction process lead to significant false-positive rates, which eventually diminish the accuracy aspects of the system. In this study, a sophisticated deep learning approach is employed for feature extraction which improves the nodule localization or identification stage of the system. Further, the false positives sneaking out of the system are drastically reduced by adopting a Conditional Random Framework in the model. The quantitative demonstrations prove the efficiency of the model to detect sub-solid nodules in CT images. Thus the employability of the model for early detection of the nodules is tested and verified.