Adenoma Dysplasia Grading of Colorectal Polyps Using Fast Fourier Convolutional ResNet (FFC-ResNet)

Abstract

Colorectal polyps are precursor lesions of colorectal cancer; hence, early detection and dysplasia grading of polyps are essential for determining cancer risk, the possibility of developing subsequent polyps, and follow-up recommendations. The significant contribution of this study is the development of an enhanced deep-learning model called Fast Fourier Convolutional ResNet (FFC-ResNet) to classify dysplasia grades of polyps. It is based on the ResNet-50 architecture and uses cross-feature fusion, which combines local features extracted using conventional spatial convolution with global features extracted using Fourier convolution. Because of the compensatory effect between local and global features, the learnability and performance of FFC-ResNet have increased. The proposed FFC-ResNet was developed and tested using UniToPatho, a dataset containing 7000 μm and 800 μm hematoxylin-and-eosin (H&E)-stained colorectal images. And a favourable performance of sensitivity 0.95, specificity 0.93, balance accuracy 0.94, precision 0.95, F1 score 0.95 and AUC 0.99 was obtained using 800 μm polyp patches.