An automatic system to detect colorectal polyp using hybrid fused method from colonoscopy images

Abstract

The third most common disease in the world that leads to mortality is colorectal cancer (CRC). Colonoscopy, which locates and removes colonic polyps, is the most common procedure worldwide for the early identification and preventative treatment of colorectal cancer. The likelihood of a CRC patient dying can be considerably decreased by early diagnosis and treatment of precancerous polyps. Unidentified polyps may eventually turn into cancer. Although several classification algorithms have been put out to identify polyps, their effectiveness has not yet been compared to that of skilled endoscopes. An ensemble machine learning-based classification approach for detecting colorectal cancer from colonoscopy images is presented in this research. Firstly researchers collect colorectal cancer or polyp images from three standard datasets. In the preprocessing phase, researcher convert the RGB image to grayscale image and identified the region of interest (ROI) by removing the unwanted regions. A hybrid anisotropic diffusion filtering (HADF) approach was employed to eliminate noise from each image. Then the system extracts features from individual feature extractor methods and fused extracted features in a vector. The fused features help to detect colorectal polyp or cancer for increasing cancer or polyp identification rates. Finally, an ensemble classifier classifies the colorectal cancer or normal images and achieves better accuracy. The suggested technique exceeds the prior conventional methods, according to tests on widely used public datasets, improving accuracy by 99.45 %, sensitivity by 99.30 %, specificityby 99.58 %, and precision 99.53 %.

Keywords

• Binary differential evolution (BDE)
• Colorectal cancer (CRC)
• Colonoscopy image
• Convolutional neural network (CNN)
• Empirical mode decomposition (EMD)
• Ensemble classifier