AGFC - Augmenting Image Analysis: Gradient Magnitude from a Smoothed Image for Improved Feature Detection in Colorectal Imagery
Keywords:
Colorectal Cancer, HCNN, Multilayer Perception, MLP, SCNN.Abstract
This research presents a comprehensive framework for the processing and classification of multi-modal colorectal images, leveraging an extensive array of data augmentation, neural network models, and advanced techniques. The multi-level classification pipeline commences with a Sequential Convolutional Neural Network (SCNN) and progresses to the subsequent stage, featuring an abnormal tissue detection module incorporating excess object removal and transformers. The architecture further integrates a hybrid Convolutional Neural Network (HCNN), encompassing a Vision Transformer (ViT), a custom cross-modality transformer, a traditional CNN, a Multilayer Perception (MLP), and a combined model. The apex of this approach materializes in a final multi-modal classifier, validating testing images and executing classification tasks. This framework not only showcases a sophisticated and effective strategy for multi-modal colorectal image processing but also exhibits the potential to augment the precision and generalization of Colorectal Cancer (CRC) risk assessments. The incorporation of diverse imaging modalities and advanced neural network architectures positions this method as a robust tool for refining the accuracy of CRC risk predictions in clinical applications.
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