Strengthening Deep Learning Model for Robust Screening of Volumetric Chest Radiographic Scans

Abstract

The emerging deep learning algorithms have shown significant potential in the development of efficient computer-aided diagnosis tools for automated detection of lung infections using chest radiographs. However, many existing methods are slice-based and require manual selection of appropriate slices from the entire CT scan, which is tedious and requires expert radiologists. To overcome these limitations, we propose a recurrent 3D Inception network (R3DI-Net) that sequentially exploits spatial and 3D structural features of the entire CT scan, ultimately leading to improved diagnostic performance. Additionally, the proposed method flexibly handles input CT scans with a variable number of slices without incurring performance degradation. A quantitative evaluation of R3DI-Net was made using a combined collection of three publicly accessible datasets containing a sufficient number of data samples. Our method outperforms various existing methods by achieving remarkable performances of 98.39%, 98.36%, 98.1%, and 98.64% in terms of accuracy, F1-score, sensitivity, and average precision, respectively.

Keywords Deep learning, Degradation, Three-dimensional displays, Sensitivity, Computed tomography, Image processing, Lung.