TY - GEN
T1 - Strengthening Deep Learning Model for Robust Screening of Volumetric Chest Radiographic Scans
AU - Owais, Muhammad
AU - Hassan, Taimur
AU - Gour, Neha
AU - Ganapathi, Iyyakutti Iyappan
AU - Werghi, Naoufel
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - computer-aided diagnosis
KW - lung infection
KW - R3DI-Net
KW - radiology
UR - http://www.scopus.com/inward/record.url?scp=85180811458&partnerID=8YFLogxK
U2 - 10.1109/ICIP49359.2023.10222612
DO - 10.1109/ICIP49359.2023.10222612
M3 - Conference contribution
AN - SCOPUS:85180811458
T3 - Proceedings - International Conference on Image Processing, ICIP
SP - 1545
EP - 1549
BT - 2023 IEEE International Conference on Image Processing, ICIP 2023 - Proceedings
PB - IEEE Computer Society
T2 - 30th IEEE International Conference on Image Processing, ICIP 2023
Y2 - 8 October 2023 through 11 October 2023
ER -