10.1016/j.scitotenv.2021.146956 Detection of morphological changes caused by chemical stress in the cyanobacterium Planktothrix agardhii using convolutional neural networks. [Dataset] Carloto, Ismael Ismael Carloto Johnston, Pamela Pamela Johnston Pestana, Carlos J. Carlos J. Pestana Lawton, Linda A. Linda A. Lawton Elsevier 2021 Cyanobacteria; CNN; Water treatment; Hydrogen peroxide; Image recognition; Image segmentation 2021-03-31 2021-03-31 Dataset The presence of harmful algal bloom in many reservoirs around the world, alongside the lack of sanitation law/ordinance regarding cyanotoxin monitoring (particularly in developing countries), create a scenario in which the local population could potentially chronically consume cyanotoxin-contaminated waters. Therefore, it is crucial to develop low cost tools to detect possible systems failures and consequent toxin release inferred by morphological changes of cyanobacteria in the raw water. This paper aimed to look for the best combination of convolutional neural network (CNN), optimizer and image segmentation technique to differentiate P. agardhii trichomes before and after chemical stress caused by the addition of hydrogen peroxide. The file accompanying this output contains supplementary figures and tables of results obtained from the experiments in this study.