Attention-Guided Transfer Learning for Identification of Filamentous Fungi Encountered in the Clinical Laboratory.

ABSTRACT: This study addresses the challenge of accurately identifying filamentous fungi in medical laboratories using transfer learning with convolutional neural networks (CNNs). The study uses microscopic images from touch-tape slides with lactophenol cotton blue staining, the most common method in clinical settings, to classify fungal genera and identify Aspergillus species. The training and test data sets included 4,108 images with representative microscopic morphology for each genus, and a soft attention mechanism was incorporated to enhance classification accuracy. As a result, the study achieved an overall classification accuracy of 94.9% for four frequently encountered genera and 84.5% for Aspergillus species. One of the distinct features is the involvement of medical technologists in develo

SUBMITTER: Huang TS 

PROVIDER: S-EPMC10269873 | biostudies-literature | 2023 Jun

REPOSITORIES: biostudies-literature