Transfer learning for image-based classification of filamentous fungi using culture morphology and microscopy images

Susy Mathew; Jon Davis; Rebecca Davis

doi:10.1016/j.pathol.2026.01.311

February 1, 2026Pathology

Transfer learning for image-based classification of filamentous fungi using culture morphology and microscopy images

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Key Points

The aim is to enhance the identification of filamentous fungi through automated image classification techniques.
Utilized culture morphology and microscopy images for analysis
Applied transfer learning techniques using pre-trained convolutional neural networks (CNNs)
Conducted image-based classification to automate filamentous fungi identification.
Demonstrated effective classification of filamentous fungi using CNNs
Achieved higher accuracy in automated identification
Highlighted the potential for using transfer learning in mycology.

Implication

Transfer learning improves image classification of filamentous fungi using culture morphology and microscopy images, suggesting greater automation capabilities.

Abstract

Background: Culture morphology and microscopy images are required for identification of moulds. Automated image classification is achievable in specific domains via transfer learning of pre-trained convolutional neural networks (CNNs).

Discussion

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Cite This Study

Mathew et al. (2026) studied this question.

synapsesocial.com/papers/699a534bfdaf4e3c1268eeb2 https://doi.org/10.1016/j.pathol.2026.01.311

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Transfer learning for image-based classification of filamentous fungi using culture morphology and microscopy images

Key Points

Implication

Abstract

Discussion

Cite This Study

Transfer learning for image-based classification of filamentous fungi using culture morphology and microscopy images

Key Points

Implication

Abstract

Discussion

Cite This Study

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