Classification of patterns of benignity and malignancy based on CT using topology-based phylogenetic diversity index and convolutional neural network

Lung cancer has been recognized as the primary global cause of death among cancer patients. This work is intended to develop a methodology for diagnosis of lung nodules using images from the Lung Image Database Consortium and Image Database Resource Initiative. The proposed method uses image processing and pattern recognition techniques. To differentiate the patterns of malignant and benign forms, we used index basic taxic weights and standardized taxic weights. Finally, we applied a convolutional neural network for classification. In the test stage, we applied the proposed methodology to 50,580 (14,184 malignant and 36,396 benign) nodules from the image database. The proposed method presents promising results for the diagnosis of malignancy and benignity, achieving an accuracy of 92.63%, sensitivity of 90.7%, specificity of 93.47%, and receiver operating characteristic curve of 0.934. These results are promising and demonstrate a real rate of correct detections using the texture features. Because precocious detection allows faster therapeutic intervention, and thus a more favorable prognosis for the patient, we propose herein a methodology that contributes to the field in this aspect.