Introduction to the special section on Artificial Intelligence-driven Mobile Edge Computing (VSI-aime)

Abstract

This work presents a novel method that uses a Vector of Pre-processing Filters combined with simple relational and Boolean equations for pulmonary nodule detection. To isolate nodules from other lung structures, we propose a 16 filter scheme endowed with multiscale median masks, statistical-based thresholds, and 3-D morphological operations. In the Boolean False Positive Reduction stage, relational and Boolean equations select nodule candidates from pre-processing filters using our descriptor with 20 attributes. Finally, a Convolutional Neural Network (CNN) classifies the remaining structures into nodule or no nodule. The method reached 92.75% sensitivity for an average of 8 false positives per exam using all exams in the public Lung Image Database Consortium (LIDC) with a slice thickness of less than 2 mm that contains lesions larger than 3 mm marked “nodule” by at least 3 radiologists. For a less consensual gold standard, our method reaches the highest sensitivity levels among listed results.