TY - GEN
T1 - UNet with Two-Fold Training for Effective Segmentation of Lung Section in Chest X-Ray
AU - Rajinikanth, Venkatesan
AU - Kadry, Seifedine
AU - Damasevicius, Robertas
AU - Gnanasoundharam, J.
AU - Abed Mohammed, Mazin
AU - Glan Devadhas, G.
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Segmentation and evaluation of the Region of Interest (ROI) in medical imaging is a prime task for disease screening and decision-making. Due to accuracy, Convolutional-Neural-Network (CNN) based ROI segmentation has been widely employed in recent years to evaluate a class of medical images recorded using chosen modality. The proposed work aims to demonstrate the segmentation performance of the UNet scheme with a one-fold and two-fold training process. To experimentally verify the merit of the proposed scheme, segmentation of the lung section from the chest X-ray is studied. This research includes the following parts; (i) Resizing the test image and image mask to pixels, (ii) Training the UNet with one-fold and two-fold approaches, (iii) Extracting the ROI, (iv) Comparing the ROI with the mask to compute the image metrics and (v) Validating and confirming the segmentation performance of UNet. The performance of UNet is then verified with UNet+ and UNet++. The investigational ending substantiates that the proposed approach helps to get better Jaccard (>95%), Dice ((>97%), and Accuracy (>98%) in two-fold training compared to other methods considered in this study.
AB - Segmentation and evaluation of the Region of Interest (ROI) in medical imaging is a prime task for disease screening and decision-making. Due to accuracy, Convolutional-Neural-Network (CNN) based ROI segmentation has been widely employed in recent years to evaluate a class of medical images recorded using chosen modality. The proposed work aims to demonstrate the segmentation performance of the UNet scheme with a one-fold and two-fold training process. To experimentally verify the merit of the proposed scheme, segmentation of the lung section from the chest X-ray is studied. This research includes the following parts; (i) Resizing the test image and image mask to pixels, (ii) Training the UNet with one-fold and two-fold approaches, (iii) Extracting the ROI, (iv) Comparing the ROI with the mask to compute the image metrics and (v) Validating and confirming the segmentation performance of UNet. The performance of UNet is then verified with UNet+ and UNet++. The investigational ending substantiates that the proposed approach helps to get better Jaccard (>95%), Dice ((>97%), and Accuracy (>98%) in two-fold training compared to other methods considered in this study.
KW - Chest X-ray
KW - Lung segmentation
KW - Two-fold training
KW - UNet
KW - Validation
UR - https://www.scopus.com/pages/publications/85141406127
U2 - 10.1109/ICICICT54557.2022.9917585
DO - 10.1109/ICICICT54557.2022.9917585
M3 - Conference contribution
AN - SCOPUS:85141406127
T3 - Proceedings of the 2022 3rd International Conference on Intelligent Computing, Instrumentation and Control Technologies: Computational Intelligence for Smart Systems, ICICICT 2022
SP - 977
EP - 981
BT - Proceedings of the 2022 3rd International Conference on Intelligent Computing, Instrumentation and Control Technologies
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 3rd International Conference on Intelligent Computing, Instrumentation and Control Technologies, ICICICT 2022
Y2 - 11 August 2022 through 12 August 2022
ER -