OBJECTIVE: The main goal is the early detection of neoplasia patterns through the quantification of DNA in Feulgen-stained sample images through deep learning methods. STUDY DESIGN: Comparison of U-Net and Attention U-Net for image semantic segmentation, employing ResNet18 and ResNet34 as their backbones. The presented approaches for both binary and multi-class segmentation tasks were also verified. The evaluation was conducted at two image resolutions: 600×800 pixels and 1200×1600 pixels, resulting in a total of 16 experiments. Our dataset contains 1, 010 images. RESULTS: Best results were achieved with Attention U-Net employing a ResNet18 as its backbone, with 600× 800 as image resolution. This architecture achieved an intersection over union (IoU) of 0.809. In the multiclass segmentation, the best results were achieved with the U-Net, employing a ResNet18 as its backbone, and an image field resolution of 600×800, resulting in an IoU of 0.638. CONCLUSION: Semantic segmentation using convolutional neural networks (CNNs) showed to be a robust approach. Experimental results demonstrate the validity of the use of these networks as a promising solution for the automated segmentation of cell nuclei. These approaches have potential to be employed as a step of a possible pipeline for nuclei segmentation. © 2021 Science Printers and Publishers Inc.. All rights reserved.