Abstract
Thoracic computed tomography (CT) technology has been used for lung cancer screening in high-risk populations, and this technique is highly effective in the identification of early lung cancer. With the rapid development of intelligent image analysis in the field of medical science and technology, many researchers have proposed computer-aided automatic diagnosis methods for facilitating medical experts in detecting lung nodules. This paper proposes an advanced clinical decision-support system for analyzing chest CT images of lung disease. Three advanced methods are utilized in the proposed system: the three-stage automated segmentation method (TSASM), the discrete wavelet packets transform (DWPT) with singular value decomposition (SVD), and the algorithms of the rough set theory, which comprise a classification-based method. Two collected medical CT image datasets were prepared to evaluate the proposed system. The CT image datasets were labeled (nodule, non-nodule, or inflammation) by experienced radiologists from a regional teaching hospital. According to the results, the proposed system outperforms other classification methods (trees, naïve Bayes, multilayer perception, and sequential minimal optimization) in terms of classification accuracy and can be employed as a clinical decision-support system for diagnosing lung disease.
Highlights
In recent years, substantial difficulties have been encountered in the treatment of lung cancer, which have attracted increasing attention in medical research
computed tomography (CT) technology has been used for lung cancer screening in high-risk populations, and this technique is highly effective in the identification of early lung cancer [4]
This paper proposes a clinical decision-support system based on three advanced image analysis and classification methods for facilitating the diagnosis process and improve accuracy: (1) a three-stage automated segmentation method (TSASM) for outlining the region of segmentation interest (ROI), (2) a discrete wavelet packet transform (DWPT) with singular value decomposition (SVD) [10,11,12] for extracting the image features of the ROI, and (3) the rough set theory (RST) [13,14,15,16] as a classification method for the classification and diagnosis of pulmonary diseases based on chest CT images
Summary
Substantial difficulties have been encountered in the treatment of lung cancer, which have attracted increasing attention in medical research. We argue that an advanced clinical support system should be able to produce high-accuracy diagnostic results with traceable rules Such a system would be more likely to be trusted by doctors and medical image specialists than systems without such rules. This paper proposes a clinical decision-support system based on three advanced image analysis and classification methods for facilitating the diagnosis process and improve accuracy: (1) a three-stage automated segmentation method (TSASM) for outlining the ROI, (2) a discrete wavelet packet transform (DWPT) with singular value decomposition (SVD) [10,11,12] for extracting the image features of the ROI, and (3) the rough set theory (RST) [13,14,15,16] as a classification method for the classification and diagnosis of pulmonary diseases based on chest CT images
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
