Innovative Approaches to Colon Cancer Imaging: Leveraging One-Shot Learning with MobileNetV2 Feature Extraction

In the area of medical images, particularly for detecting cancer, the lack of labeled data is a major obstacle in creating strong and precise classification models. The article presents a new method for classifying colon cancer images using one-shot learning and MobileNetV2 for feature extraction to identify images from a subset of the LC25000 dataset. We demonstrate the ability of the model to obtain high classification accuracy with minimum training instances by concentrating on colon cancer and benign diseases. By employing strategic data augmentation strategies, we improve the model’s ability to generalize from limited data, which is a crucial obstacle in medical imagine analysis. The model attained an accuracy of around 90%, surpassing conventional machine learning techniques and showcasing the promise of integrating one-shot learning with deep learning structures in medical diagnostics. The research highlights the possibility of using advanced machine learning methods in situations with limited data and sets the stage for further investigation into effective diagnostic tools in other areas of medical imaging.