Kadaskar, M.Patil, N.2026-02-0620232023 14th International Conference on Computing Communication and Networking Technologies, ICCCNT 2023, 2023, Vol., , p. -https://doi.org/10.1109/ICCCNT56998.2023.10307210https://idr.nitk.ac.in/handle/123456789/29380Cell nuclei segmentation is crucial for developing digital pathology and medical research. It is helpful in numerous applications, including illness diagnostics and other medical therapies. Unfortunately, due to the massive number of nuclei, manual analysis of these image slides takes time and effort. Morphometric appearances add to the complexity. Nevertheless, we upgraded the Nested UNet with an EfficientNetV2S backbone and added a CBAM module in the decoder layers to obtain state-of-the-art performance in medical imaging. This improved model is easier to train, and attention blocks help to tune the retrieved features for better performance. We tested our model using the CryoNuSeg dataset to see how well it performed. Our model scores 0.941 on Dice, 0.605 on AJI, and 0.614 on PQ. © 2023 IEEE.CBAMDeep LearningEfficientNetV2Nested UNetNuclei Segmentation