Robust Few Shot Biological Pathology Classification via Optimized Contrastive MobileNetV2: A Transferable Model for Low Resource Medical Imaging

Artificial intelligence has revolutionized computational diagnostics, however deploying reliable intelligent systems in extreme low-resource environments remains a critical structural challenge in health informatics. Conventional deep learning architectures, such as standard Convolutional Neural Net...

Täydet tiedot

Bibliografiset tiedot

Päätekijät:	Nurul Adi Prawira, Muhammad Firmansyah, Dhendra Marutho, Achraf Ouhab
Aineistotyyppi:	Artigo
Kieli:	Inglês
Julkaistu:	Universitas Muhammadiyah Semarang 2026-03-01
Sarja:	Journal of Intelligent Computing and Health Informatics
Aiheet:	few-shot learning contrastive learning mobilenetv2 health informatics edge computing cross-domain proxy
Linkit:	https://jurnal.unimus.ac.id/index.php/ICHI/article/view/20179
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