Cancer is one of the very dangerous diseases which cause lots of death all over the world. There are various types of cancers which affect male, female even children too. Among various types of cancer, the cervical cancer is one types which is found in women and which is a major cause of death in women, it is a very complicated disease but if it is detected at an early stage then we can limit its complications. It is possible to detect cervical cancer at an early stage with the help of various ways and means. There are categories of symptom-based, diagnosis-based, and many other ways the cervical cancer can be detected among women. Here in this research paper shows the use of ResNet50 and MobileNetv2 for diagnosis or detection of cervical cancer using image processing and deep learning approach. Here, after applying both the deep learning-based approaches, their comparative analysis is also performed to determine their efficiency in delivering the result in term of diagnosis the cervical cancer. To conduct this study cell images were fed into the CNN models, and they were trained to evaluate which model provides more accuracy than the other, five classes of images that were created are as follows Dyskeratotic, Koilocytotic, Metaplastic, Parabasal, Superficial-Intermediate, and the models were trained based on these five classes. After various preprocessing and augmentation methods that were conducted on the images, the models were trained, and the results showed that MobileNet-V2 was able to achieve 87% model accuracy and validation accuracy of 86% which is higher than compared to Resnet50 which was 82% Model accuracy and validation accuracy of 64%.