An Automated Framework for Detection, Localization, and Classification of Colonic Polyp using Deep Learning

Abstract

Abstract Colorectal cancer (CRC) in its advanced stage is one of the leading causes of death worldwide. However, early detection of polyps which are the precursor to such cancer can lead to better prognosis and clinical management. This report proposes an automated diagnostic technique to detect, localize, and classify polyps in colonoscopy video frames. Manual detection and localization of polyps on hugely acquired colonic frames have many limitations. Our deep learning-based framework proposes an attention-based YOLOv4 detector for polyp detection and localization. Finally, leveraging a fusion of deep and handcrafted features of the polyps, the detected polyps are classified as benign or malignant. The individual and the cross-database performances on two databases suggest the robustness of our method in polyp localization. The comparison of our approach based on significant clinical parameters with current state-of-the-art methods confirms that our method can be used for automated polyp localization in both real-time and offline colonoscopic video frames. Our method can give an average precisionof 0.8971 and 0.9171 and an average IoU of 0.8325 and 0.8179 for the Kvsir-SEG and SUN databases, respectively. Similarly, our proposed classification framework on the detected polyps yields a classification accuracy of 96.66% on a public dataset.