Image dataset of urine test results on petri dishes for deep learning classification

Recent advancements in image analysis and interpretation technologies using computer vision techniques have shown potential for novel applications in clinical microbiology lab-oratories to support task automation aiming for faster and more reliable diagnostics. Deep learning models can be a valuable tool in the screening process, helping technicians spend less time classifying no-growth results and quickly separating the categories of tests that deserve further anal-ysis. In this context, creating datasets with correctly clas-sified images is fundamental for developing and improving such models. Therefore, a dataset of urine test Petri dishes images was collected following a standardized process, with controlled conditions of positioning and lighting. Image ac-quisition was conducted by applying a hardware chamber equipped with a led lightning source and a smartphone cam-era with 12 MP resolution. A software application was de-veloped to support image classification and handling. Experi-enced microbiologists classified the images according to the positive, negative, and uncertain test results. The resulting dataset contains a total of 1500 images and can support the development of deep learning algorithms to classify urine ex-ams according to their microbial growth.(c) 2023 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license ( http://creativecommons.org/licenses/by/4.0/ )