AI-based image analysis and disease identification are getting more and more popular. Examples of these disorders include mouth cancer, cer-vical cancer, retinal glucose analysis, etc. Capturing images of the af-fected areas with customized camera modules is a common method of gathering data. Like any other data source, a technique that is prone to error and may contain undesirable objects and regions that need to be cleaned up by removal is known as a removal procedure. Outliers in these kinds of datasets can have a detrimental effect on the evaluation of the effectiveness of machine learning models. It would take a lot of effort to manually clean data, especially if it was gathered from multi-ple sources. As a result, cleaning the data is essential before model training. In this study, we incorporate reviewing earlier works to un-leash the gaps and ideas behind the few-shot image classification mod-els. This research paper mainly focuses on the identification of the pos-sibility of implementing the hybrid model to clean the few-shot images. This hybrid model is built using two neural network models, for exam-ple, transformers. Generators or by using neural networks.