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

• Published in: Data in brief Vol. 47; p. 109034
• Main Authors: da Silva, Gabriel Rodrigues, Rosmaninho, Igor Batista, Zancul, Eduardo, de Oliveira, Vanessa Rita, Francisco, Gabriela Rodrigues, dos Santos, Nathamy Fernanda, de Mello Macêdo, Karin, da Silva, Amauri José, de Lima, Érika Knabben, Lemo, Mara Elisa Borsato, Maldonado, Alessandra, Moura, Maria Emilia G., da Silva, Flávia Helena, Guimarães, Gustavo Stuani
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier Inc 01.04.2023; Elsevier
• Subjects: Computational Vision; Data; Image Classification; Petri Dish; Urine Test Classification; Image Classification; Computational Vision; Petri Dish; Urine Test Classification
• ISSN: 2352-3409; 2352-3409
• DOI: 10.1016/j.dib.2023.109034

Recent advancements in image analysis and interpretation technologies using computer vision techniques have shown potential for novel applications in clinical microbiology laboratories 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 analysis. In this context, creating datasets with correctly classified 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 acquisition was conducted by applying a hardware chamber equipped with a led lightning source and a smartphone camera with 12 MP resolution. A software application was developed to support image classification and handling. Experienced 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 exams according to their microbial growth.