Abstract
Significant progress has been made to leverage
machine learning towards decision support diagnosis from
ultrasound in medical imaging. Due to the continuous challenges
associated with evolving characteristics of COVID-19, the
availability of fast, safe, and highly sensitive diagnostic tools is
imperative. Where ultrasound imaging has been proven superior
to X-rays and CT scans, the main limitation to its use is operator
dependency and experience. In this study, we propose a novel
transfer learning and semantic segmentation framework for
automatic detection and localization of multiple lung pathologies
in Lung Ultrasound images. The proposed framework allows
better interpretation and explanation of the model decisions, with
clear visualization and localization of the different pathologies.
The experimental evaluation of the proposed framework was done
on an open-source Lung ultrasound imaging dataset which is
labeled and annotated by team of radiologists. The proposed
approach was validated using other benchmarks models for
comparison in terms of DICE coefficients at 0.98 and IOU Score
at 0.97 which outperforms the other benchmark models quite
significantly. This provides interpretation of the reasoning behind
the decision made by the model leading to higher rates of
acceptance with clinicians.
machine learning towards decision support diagnosis from
ultrasound in medical imaging. Due to the continuous challenges
associated with evolving characteristics of COVID-19, the
availability of fast, safe, and highly sensitive diagnostic tools is
imperative. Where ultrasound imaging has been proven superior
to X-rays and CT scans, the main limitation to its use is operator
dependency and experience. In this study, we propose a novel
transfer learning and semantic segmentation framework for
automatic detection and localization of multiple lung pathologies
in Lung Ultrasound images. The proposed framework allows
better interpretation and explanation of the model decisions, with
clear visualization and localization of the different pathologies.
The experimental evaluation of the proposed framework was done
on an open-source Lung ultrasound imaging dataset which is
labeled and annotated by team of radiologists. The proposed
approach was validated using other benchmarks models for
comparison in terms of DICE coefficients at 0.98 and IOU Score
at 0.97 which outperforms the other benchmark models quite
significantly. This provides interpretation of the reasoning behind
the decision made by the model leading to higher rates of
acceptance with clinicians.
Original language | English |
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Title of host publication | Proceedings IEEE of the 2024 11th International Conference on Soft Computing & Machine Intelligence (ISCMI) |
Editors | Punam Bedi, Tom Gedeon, Suash Deb, Kazem Abhary, Girija Chetty, Marde Helbig, Gang Li, Sona Taheri, Mahua Bhattacharya, Efrén Mezura-Montes, Ka Chun Wong |
Place of Publication | United States |
Publisher | IEEE, Institute of Electrical and Electronics Engineers |
Pages | 328-331 |
Number of pages | 4 |
ISBN (Electronic) | 798331518127 |
ISBN (Print) | 798331518127 |
DOIs | |
Publication status | Published - 22 Nov 2024 |
Event | 11th Intl. Conference on Soft Computing & Machine Intelligence (ISCMI 2024) - Melbourne, Australia Duration: 22 Nov 2024 → 23 Nov 2024 |
Publication series
Name | |
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ISSN (Print) | 2640-0146 |
Conference
Conference | 11th Intl. Conference on Soft Computing & Machine Intelligence (ISCMI 2024) |
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Country/Territory | Australia |
City | Melbourne |
Period | 22/11/24 → 23/11/24 |