Semantic Segmentation and Pathology Localization in Lung Ultrasound Images Using Transfer Learning

Nancy Kaur, Girija CHETTY

Research output: A Conference proceeding or a Chapter in BookConference contributionpeer-review

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.
Original languageEnglish
Title of host publicationProceedings IEEE of the 2024 11th International Conference on Soft Computing & Machine Intelligence (ISCMI)
EditorsPunam 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 PublicationUnited States
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages328-331
Number of pages4
ISBN (Electronic)798331518127
ISBN (Print)798331518127
DOIs
Publication statusPublished - 22 Nov 2024
Event11th Intl. Conference on Soft Computing & Machine Intelligence (ISCMI 2024) - Melbourne, Australia
Duration: 22 Nov 202423 Nov 2024

Publication series

Name
ISSN (Print)2640-0146

Conference

Conference11th Intl. Conference on Soft Computing & Machine Intelligence (ISCMI 2024)
Country/TerritoryAustralia
CityMelbourne
Period22/11/2423/11/24

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