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

Gabriel Rodrigues da Silva; Igor Batista Rosmaninho; Eduardo Zancul; Vanessa Rita de Oliveira; Gabriela Rodrigues Francisco; Nathamy Fernanda Dos Santos; Karin de Mello Macêdo; Amauri José da Silva; Érika Knabben de Lima; Mara Elisa Borsato Lemo; Alessandra Maldonado; Maria Emilia G Moura; Flávia Helena da Silva; Gustavo Stuani Guimarães

doi:10.1016/j.dib.2023.109034

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

Data Brief. 2023 Mar 2:47:109034. doi: 10.1016/j.dib.2023.109034. eCollection 2023 Apr.

Authors

Gabriel Rodrigues da Silva¹, Igor Batista Rosmaninho¹, Eduardo Zancul¹, Vanessa Rita de Oliveira², Gabriela Rodrigues Francisco², Nathamy Fernanda Dos Santos², Karin de Mello Macêdo², Amauri José da Silva², Érika Knabben de Lima², Mara Elisa Borsato Lemo², Alessandra Maldonado², Maria Emilia G Moura², Flávia Helena da Silva², Gustavo Stuani Guimarães²

Affiliations

¹ University of São Paulo, School of Engineering, Av. Prof. Luciano Gualberto, 1380, Cidade Universitária, São Paulo-SP, 05508-010, Brazil.
² Grupo Fleury - Clinical Analysis, Av. Morumbi, 8860, São Paulo-SP, Brazil.

Abstract

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.

Keywords: Computational Vision; Image Classification; Petri Dish; Urine Test Classification.