DOI: 10.5061/dryad.cjsxksnj4
title: Data from: Advancing mold identification in the routine laboratory: Performance of smartphone-based imaging and a newly developed Convolutional Neural Network

description: Background: Mold identification in clinical diagnostics is traditionally
 labor intensive and is dependent on expert interpretation. MoldVision is a
 deep learning approach that uses smartphone images ofmold cultures to
 automate identification. Methods: We analyzed 161 clinical isolates across
 four common mold genera. Penicillium spp., Aspergillus spp. (with A.
 flavus and A. fumigatus), Fusarium spp., and Cladosporium spp. Daily
 images were captured from the top and bottom of culture plates over five
 days using a standardized smartphone setup, generating over 4,000 images.
 We trained three variations of VGG16 convolutional neural networks (CNN)
 and benchmarked the best-performing model (VGG16 with dual classification
 heads) against LightGBM models trained on pre-extracted features and human
 expert assessments at various time points. Results: The best performing
 VGG16 model achieved a mean (SD) ROC-AUC of 92.7% ± 1.8% and sensitivity
 of 68.7% ±2.6% across all species. Here, the performance in identifying
 Cladosporium spp. was best (ROC-AUC 99.9% ± 0.1%, 5-fold cross-validation
 mean and SD ). Regarding the evaluations over time, early stage
 classification (days 1-2) was challenging (F1-score 38.8% ± 3.5% across
 all species but improved significantly on day 3-5 (F1 92.1% ± 2.8% across
 all species). Compared to experts, MoldVision consistently showed superior
 performance, particularly in mature cultures, detecting subtle
 morphological features earlier and more accurately. Conclusions: Our
 results demonstrate that CNNs integrated with low-cost smartphone imaging
 can reliably classify mold species in routine diagnostics, outperforming
 human experts in many cases. This approach offers a practical and scalable
 solution for laboratories lacking specialized mycology expertise,
 especially in resource-limited settings.
description: # Data from: Advancing mold identification in the routine laboratory:
 Performance of smartphone-based imaging and a newly developed
 Convolutional Neural Network Dataset DOI:
 [10.5061/dryad.cjsxksnj4](10.5061/dryad.cjsxksnj4) ## Description of the
 data and file structure This dataset contains image files collected from
 *Penicillium spp.*, *Aspergillus spp.* (with *Aspergillus flavus* and
 *Aspergillus fumigatus*), *Fusarium spp.*, and *Cladosporium spp.*
 isolates over a 5 day period from the top and bottom of a Sabouraud Agar
 Plate. Each file is named according to a standardized convention to
 facilitate identification and reuse. ### Files and variables #### File:
 moldvision_database.csv **Description:** A CSV file for ease of access for
 Machine Learning applications, as well as providing a database for the
 image files ##### Variables * filename * class: species of fungal mold *
 top/bottom: orientation of the agar plate in the picture ("top"
 or "bottom") * ID: three‐digit subject identifier (from 1
 upwards, e.g., 024, 003) * day: day of growth after incubation *
 rotation_index: separator index to indicate image rotation (from 1
 upwards) * time_stratification: special string to stratify into early (Day
 1-2) and late images (Day 3-5) #### File: moldvision_database.zip
 **Description:** a zip file containing all image files Filenames follow
 the pattern: ``` class_top-bottom_ID_day_rot.jpg ```
 Image format: All files are compressed JPEG (.jpg) images captured at
 3000×3000 pixels, 24‐bit depth. Each image depicts a single sample at a
 specific orientation, on a given day after incubation, with a random
 rotation angle (implied by rot index). ### Metadata and Attributes No
 separate metadata file is provided; all relevant identifiers are embedded
 in the filename.