An Enhanced Feature Neural Network and Its Application in Detection of Colorectal Polyps

Abstract

The colorectal cancer is one of the most common and lethal cancers, and colorectal polyps, as precancerous lesions, can lead to diagnostic oversight or misdiagnosis due to their varied shapes and sizes, thereby promoting the irreversible progression of colorectal cancer. We propose a YOLO based model and name it EF-YOLO. It incorporates transformer to extract contextual information about the colorectal polyps. Simultaneously, leveraging the morphological characteristics of colorectal polyps, we design a brand-new module, namely advanced multi-scale aggregation(AMSA), to replace the traditional multi-scale module. The backbone adopts deformable convolutional network-maxpool(DCN-MP) to enhance feature extraction while adaptively sampling points to better match the shapes of colorectal polyps. By combining coordinate attention(CA), this model maximizes the use of positional and channel information, more effectively extracting features of colorectal polyps, directing the model’s attention toward the colorectal polyp region. EF-YOLO has made advancement on the merged Kvasir-SEG and CVC-ClinicDB dataset. Compared to the original model, the mean average precision(mAP) of EF-YOLO increases and reaches 96.60%, meeting automated colorectal polyp detection requirements.

Keywords

colorectal polyp / YOLO / transformer / deformable convolutional network-maxpool(DCN-MP) / coordinate attention(CA)

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