Multi-modal lung ultrasound image classification by fusing image-based features and probe information

Okolo, Gabriel Iluebe; Katsigiannis, Stamos; Ramzan, Naeem

Abstract

Lung ultrasound is a widely used portable, cheap, and non-invasive medical imaging technology that can be used to identify various lung pathologies. In this work, we propose a multi-modal approach for lung ultrasound image classification that combines image-based features with information about the type of ultrasound probe used to acquire the input image. Experiments on a large lung ultrasound image dataset that contains images acquired with a linear or a convex ultrasound probe demonstrated the superiority of the proposed approach for the task of classifying lung ultrasound images as “COVID-19”, “Normal”, “Pneumonia”, or “Other”, when compared to simply using image-based features. Classification accuracy reached 99.98% using the proposed combination of the Xception pretrained CNN model with the ultrasound probe information, as opposed to 96.81% when only the pre-trained EfficientNetB4 CNN model was used. Furthermore, the experimental results demonstrated a consistent improvement in classification performance when combining the examined base CNN models with probe information, indicating the efficiency of the proposed approach.

Citation

Okolo, G. I., Katsigiannis, S., & Ramzan, N. (2022, November). Multi-modal lung ultrasound image classification by fusing image-based features and probe information. Presented at IEEE International Conference on BioInformatics and BioEngineering (BIBE 2022), Taichung, Taiwan

• Presentation Conference Type: Conference Paper (published)
• Conference Name: IEEE International Conference on BioInformatics and BioEngineering (BIBE 2022)
• Start Date: Nov 7, 2022
• End Date: Nov 9, 2022
• Acceptance Date: Sep 19, 2022
• Online Publication Date: Dec 14, 2022
• Publication Date: 2022
• Deposit Date: Sep 20, 2022
• Publicly Available Date: Apr 28, 2023
• Series ISSN: 2159-5410,2471-7819
• DOI: https://doi.org/10.1109/bibe55377.2022.00018
• Public URL: https://durham-repository.worktribe.com/output/1136028