• Parth Gaikwad
• Aditya Patil
• Shubham Nalawade
• Ms. K. P. Joshi

The effective adoption of the U-Net and ResNet50 is used to identify nuclei. The majority of the related researches have relied on conventional computer vision techniques such as morphological image analysis, data mining, and so on, however the strategy identified in this survey paper uses machine learning techniques to improve accuracy. The ResNet and U-Net is used to efficiently conduct nuclei detection on a dataset of cervical cytology pictures. The provided method derives these qualities from a precise implementation that has resulted in the realization of the training of these neural networks. These models are trained on cervical cytology images dataset that is provided as an input. The trained models are then used for deployment of the test cervical cytology images to determine the performance of these models. The experimental evaluation of the approach has been performed which has resulted in highly accurate results portrayed in this research article.

U-net, Res-Net, Image Processing, Cervical Cell and Cell nuclei.

"Detection of Cell Nuclei In Cervical Cytology Images Using U-Net And Res-Net Neural Networks", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.9, Issue 8, page no.310-316, August-2022, Available :http://www.jetir.org/papers/JETIRFP06055.pdf

"Detection of Cell Nuclei In Cervical Cytology Images Using U-Net And Res-Net Neural Networks", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.9, Issue 8, page no. pp310-316, August-2022, Available at : http://www.jetir.org/papers/JETIRFP06055.pdf