Lung image quality assessment and diagnosis using generative autoencoders in unsupervised ensemble learning

Rajasekar, Elakkiya; Chandra, Harshiv; Pears, Nick; Vairavasundaram, Subramaniyaswamy; Kotecha, Ketan

doi:10.1016/j.bspc.2024.107268

Biomedical Signal Processing and Control

2025

DOI: 10.1016/j.bspc.2024.107268

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Lung image quality assessment and diagnosis using generative autoencoders in unsupervised ensemble learning

Elakkiya Rajasekar,

Harshiv Chandra,

Nick Pears

et al.

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Bridging the Gap Between Computational Efficiency and Segmentation Fidelity in Object-Based Image Analysis

Aguiar,

Nääs,

Okano

2024

Animals

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A critical issue in image analysis for analyzing animal behavior is accurate object detection and tracking in dynamic and complex environments. This study introduces a novel preprocessing algorithm to bridge the gap between computational efficiency and segmentation fidelity in object-based image analysis for machine learning applications. The algorithm integrates convolutional operations, quantization strategies, and polynomial transformations to optimize image segmentation in complex visual environments, addressing the limitations of traditional pixel-level and unsupervised methods. This innovative approach enhances object delineation and generates structured metadata, facilitating robust feature extraction and consistent object representation across varied conditions. As empirical validation shows, the proposed preprocessing pipeline reduces computational demands while improving segmentation accuracy, particularly in intricate backgrounds. Key features include adaptive object segmentation, efficient metadata creation, and scalability for real-time applications. The methodology’s application in domains such as Precision Livestock Farming and autonomous systems highlights its potential for high-accuracy visual data processing. Future work will explore dynamic parameter optimization and algorithm adaptability across diverse datasets to further refine its capabilities. This study presents a scalable and efficient framework designed to advance machine learning applications in complex image analysis tasks by incorporating methodologies for image quantization and automated segmentation.

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Bridging the Gap Between Computational Efficiency and Segmentation Fidelity in Object-Based Image Analysis

Aguiar,

Nääs,

Okano

2024

Animals

View full text Add to dashboard Cite

show abstract

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Lung image quality assessment and diagnosis using generative autoencoders in unsupervised ensemble learning

Cited by 1 publication

References 20 publications

Bridging the Gap Between Computational Efficiency and Segmentation Fidelity in Object-Based Image Analysis

Bridging the Gap Between Computational Efficiency and Segmentation Fidelity in Object-Based Image Analysis

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