Transfer Learning Approaches for Colorectal Tumour Detection on Adapting Pre-Trained Models to Diverse Medical Imaging Datasets

Abstract

Globally, colorectal cancer (CRC) is a major source of illness and death. Increasing early detection is essential to bettering patient outcomes. Transfer learning has been a viable method for improving medical imaging analysis tasks, such as colorectal tumor identification, with the development of DL. In order to identify colorectal tumors, this research investigates various transfer learning approaches using a range of medical imaging datasets. It starts by discussing the difficulties posed by the high dimensionality of picture characteristics and the scarcity of annotated medical imaging data. The idea of transfer learning is then explored, which uses the information that pre-trained models have on larger datasets to improve performance on smaller, task-specific datasets. Also, examine several transfer learning techniques, such as domain adaptation, feature extraction, and fine-tuning, emphasizing their usefulness and relevance in diagnosing colorectal cancer. It also discusses the significance of model selection, dataset curation, and performance assessment criteria in systems based on transfer learning. Fortunately, trends, difficulties, and prospects in the discipline can be identified by thoroughly analyzing current research and methodology. Our synthesis offers insightful information for academics and practitioners looking to expand the use of transfer learning in medical imaging tasks such as colorectal tumor detection.