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Few-shot learning boosts TB detection accuracy with limited data

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a few-shot learning approach to improve tuberculosis detection from chest X-rays, addressing the challenge of domain shift between different datasets. By fine-tuning a pre-trained DenseNet121 model on a limited number of target samples from the Mendeley TB dataset, they achieved 98.36% accuracy with just 75 labeled samples per class. This study demonstrates that full fine-tuning is an effective strategy for mitigating domain shift, making it a practical approach for low-resource clinical settings. AI

IMPACT This research demonstrates a practical method for improving AI model performance in clinical settings with limited data, potentially accelerating AI adoption in healthcare.

RANK_REASON The cluster contains a research paper detailing a novel methodology for a specific task. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

Few-shot learning boosts TB detection accuracy with limited data

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The cluster contains a research paper detailing a novel methodology for a specific task. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Bidhan Biswas, Shahadat Hossain Sohag, Nabil Ashab, Soumit Kumar Kundu, Saif Mahmud Parvez ·

    Few-Shot Cross-Dataset Adaptation for Tuberculosis Detection Using DenseNet

    arXiv:2608.21427v1 Announce Type: new Abstract: Tuberculosis (TB) is one of the most common and dangerous bacterial ailments. Every year, it causes a large number of deaths worldwide. Although many deep learning models can detect tuberculosis from chest X-rays quite accurately, s…

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