Efficient Training of Colorectal Cancer Diagnosis Model through Unsupervised Learning Composite Network

Abstract

The use of machine learning algorithms for precise and effective colorectal cancer diagnosis has gained popularity as a result of advancements in medical imaging. Through the use of an Unsupervised Learning Composite Network (ULCN) model this study suggests a novel method for training a diagnosis model. Conventional training techniques for these models frequently rely on sizable labelled datasets, which can be labour and resource-intensive to create. The ULCN, on the other hand, incorporates unsupervised learning into the training process, decreasing the need on labelled input. In order to address this issue, our study offers a revolutionary method known as the DL-Kmeans (Deep Learning with Unsupervised K-Means Clustering), which combines the effectiveness of the unsupervised K-means clustering algorithm with the power of deep learning. When compared to manual screening and annotation techniques, the DL-Kmeans ability to more quickly refine medical images serves as evidence of its effectiveness. The use of DL- Kmeans processed photos resulted in a two-fold acceleration in the training process for deep learning models used to diagnose colorectal cancer, which is very notable. Notably, this speeding up did not degrade performance quality; the DL- Kmeans enhanced method showed superior results in terms of training loss and accuracy attained. DL- Kmeans importance goes beyond only improving images. It is a useful tool for handling the growing volume of medical photos, which will help with the later creation of artificial intelligence models.