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Few-shot learning pipeline aids Monkeypox skin disease classification with CNNs

By PulseAugur Editorial · Summary by gemini-2.5-flash-lite from 2 sources

Researchers have developed a few-shot learning pipeline to classify Monkeypox and similar skin diseases using Convolutional Neural Networks (CNNs). This approach addresses the challenge of limited annotated data for rare conditions by employing a lightweight classifier called SimpleShot with pretrained CNN backbones. Experiments across multiple datasets and configurations showed that MobileNetV2_100 performed best, and cross-dataset evaluations highlighted the importance of domain robustness for clinical deployment. AI

Summary written by gemini-2.5-flash-lite from 2 sources. How we write summaries →

IMPACT Demonstrates a practical method for applying AI to rare disease classification with limited data, potentially improving diagnostic capabilities in underserved areas.

RANK_REASON Academic paper detailing a novel few-shot learning pipeline for medical image classification.

Read on arXiv cs.CV →

COVERAGE [2]

  1. arXiv cs.CV TIER_1 · Md. Safirur Rashid, Sabbir Ahmed, Muhammad Usama Islam, Sumona Hoque Mumu, Md. Hasanul Kabir ·

    Few-Shot Learning Pipeline for Monkeypox Skin Disease Classification Using CNN Feature Extractors

    arXiv:2605.05034v1 Announce Type: new Abstract: Despite the strong performance of Convolutional Neural Networks (CNNs) in disease classification, their effectiveness often depends on access to large annotated datasets, which is an impractical requirement for emerging or rare cond…

  2. arXiv cs.CV TIER_1 · Md. Hasanul Kabir ·

    Few-Shot Learning Pipeline for Monkeypox Skin Disease Classification Using CNN Feature Extractors

    Despite the strong performance of Convolutional Neural Networks (CNNs) in disease classification, their effectiveness often depends on access to large annotated datasets, which is an impractical requirement for emerging or rare conditions such as Monkeypox. To overcome this limit…

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