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

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageBritish English
Title of host publication2023 IEEE International Conference on Image Processing, ICIP 2023 - Proceedings
PublisherIEEE Computer Society
Pages1545-1549
Number of pages5
ISBN (Electronic)9781728198354
DOIs
StatePublished - 2023
Event30th IEEE International Conference on Image Processing, ICIP 2023 - Kuala Lumpur, Malaysia
Duration: 8 Oct 202311 Oct 2023

Publication series

NameProceedings - International Conference on Image Processing, ICIP
ISSN (Print)1522-4880

Conference

Conference30th IEEE International Conference on Image Processing, ICIP 2023
Country/TerritoryMalaysia
CityKuala Lumpur
Period8/10/2311/10/23

Keywords

  • computer-aided diagnosis
  • lung infection
  • R3DI-Net
  • radiology

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