Abstract
Introduction Lung cancer remains the leading cause of cancer mortality worldwide, with one of the lowest survival rates after diagnosis. Therefore, early detection greatly increases the chances of improving patient survival. Methods This study proposes a method for diagnosis of lung nodules in benign and malignant tumors based on image processing and pattern recognition techniques. Taxonomic indexes and phylogenetic trees were used as texture descriptors, and a Support Vector Machine was used for classification. Results The proposed method shows promising results for accurate diagnosis of benign and malignant lung tumors, achieving an accuracy of 88.44%, sensitivity of 84.22%, specificity of 90.06% and area under the ROC curve of 0.8714. Conclusion The results demonstrate the promising performance of texture extraction techniques by means of taxonomic indexes combined with phylogenetic trees. The proposed method achieves results comparable to those previously published.
Highlights
Lung cancer remains the leading cause of cancer mortality worldwide, with one of the lowest survival rates after diagnosis
The first considers the abundance of the species and taxonomic relationship between them, while the second represents the mean taxonomic distance between two individuals of different species. These indexes are based on phylogenetic distance, considering the architecture of a rooted tree in the form of an inclined cladogram. The use of these indexes as texture descriptors is due to the promising results published by Carvalho et al (2016) for classification of lung regions extracted from computerized tomography (CT) images as nodule and non-nodule
Proposed a method for classification of lung regions extracted from CT images as nodule and non-nodule using different diversity indexes such as taxonomic diversity and taxonomic distinction, to improve the performance of computer-aided detection (CADe) system
Summary
Lung cancer is the most frequent of all malignant tumors and has an increase of 2% per year in its worldwide incidence. According to Gerlinger et al (2012), intratumor heterogeneity may foster tumor evolution and adaptation, and, assist in the lung cancer diagnosis Another reason to use only texture features is because marking by experts are always greater than the real area of the nodule, making shape-based analysis on more difficult. These indexes are based on phylogenetic distance, considering the architecture of a rooted tree in the form of an inclined cladogram The use of these indexes as texture descriptors is due to the promising results published by Carvalho et al (2016) for classification of lung regions extracted from CT images as nodule and non-nodule. As an improvement of the methodology published by Carvalho et al (2016), we propose a method using the same indexes applied to nodules and regions generated by internal and external masks to differentiate malignancy of lung nodules on CT images
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