Convolutional Neural Networks in Computer-Aided Diagnosis of Colorectal Polyps and Cancer: A Review

Abstract

As a relatively high percentage of adenoma polyps are missed, a computer-aided diagnosis (CAD) tool based on deep learning can aid the endoscopist in diagnosing colorectal polyps or colorectal cancer in order to decrease polyps missing rate and prevent colorectal cancer mortality. Convolutional Neural Network (CNN) is a deep learning method and has achieved better results in detecting and segmenting specific objects in images in the last decade than conventional models such as regression, support vector machines or artificial neural networks. In recent years, based on the studies in medical imaging criteria, CNN models have acquired promising results in detecting masses and lesions in various body organs, including colorectal polyps. In this review, the structure and architecture of CNN models and how colonoscopy images are processed as input and converted to the output are explained in detail. In most primary studies conducted in the colorectal polyp detection and classification field, the CNN model has been regarded as a black box since the calculations performed at different layers in the model training process have not been clarified precisely. Furthermore, I discuss the differences between the CNN and conventional models, inspect how to train the CNN model for diagnosing colorectal polyps or cancer, and evaluate model performance after the training process.

White blood cell image analysis for infection detection based on virtual hexagonal trellis (VHT) by using deep learning

“…High-gradient-based data 24,37 has made significant contribution to image classification. The implications of these methods extend to image classification 38,39 , object detection 40 , boundary extraction 41 , edge detection 42 , and image segmentation 43 .…”

Pathological Insights: Enhanced Vision Transformers for the Early Detection of Colorectal Cancer

“…Moreover, most state-of-the-art deep learning studies for early detection of CRC are solely based on convolutional neural networks (CNNs) [28][29][30]. CNNs can learn visual representations for easy transfer and strong performance, owing to the strong inductive bias of spatial equivariance and translational invariance provided by their convolutional layers [31][32][33][34][35]. However, vision transformers (ViTs) exhibit superior performance over CNNs for natural image classification and segmentation [36][37][38][39].…”