Automated Detection of Helium Bubbles in Irradiated X-750

•Manual classification of microscopy images is often a tedious and time consuming process with little reproducibility.•Neural networks offer the ability to augment this classification with an automated method.•This creates self-consistency not seen in manual classification as well as realized time savings of orders of magnitude.•Neural networks exhibit promise across a wide array of microscopy disciplines. Imaging nanoscale features using transmission electron microscopy is key to predicting and assessing the mechanical behavior of structural materials in nuclear reactors. Analyzing these micrographs is often a tedious and labour intensive manual process. It is a prime candidate for automation. Here, a region-based convolutional neural network is adapted to detect helium bubbles in micrographs of neutron-irradiated Inconel X-750 reactor spacer springs. We demonstrate that this neural network produces analyses of similar accuracy and reproducibility to that produced by humans. Further, we show this method as being four orders of magnitude faster than manual analysis allowing for generation of significant quantities of data. The proposed method can be used with micrographs of different Fresnel contrasts and magnification levels.