Colon cancer stage detection in colonoscopy images using YOLOv3 MSF deep learning architecture

• Published in: Biomedical signal processing and control 2023-02, Vol.80, p.104283, Article 104283
• Main Authors: Murugesan, Malathi, Madonna Arieth, R., Balraj, Shankarlal, Nirmala, R.
• Format: Article
• Language: English
• ISSN: 1746-8094; 1746-8108
• DOI: 10.1016/j.bspc.2022.104283

•Our research uses the single-stage YOLOv3 object detection model to locate and classify the various stages based on the TL value. It outputs the class probability and position coordinates of each image by enhancing accuracy and robustness.•At the outset, during preprocessing to address the specific problems like reduced contrast, blurred images and noisy data, the proposed model defines a data augmentation with a jitter value of 0.3 to avoid overfitting.•Next, for detecting and annotating various stages of colon cancer, YOLOv3-MSF deep learning architecture with multiscale detection layers used here. By adopting transfer learning, we used YOLOv3 in our work. It chooses the anchor boxes using the K-Medoids algorithm during object detection in the training stage based on the ResNet network structure of YOLOv3.•Finally, additional layers such as fully connected layer are embedded into the existing architecture. we used the CVC colonDB database to train and evaluate the fully connected layer during experiments. To analyze the performance of the proposed system, we compare our model with the state of art object detection model. Colo-Rectal Cancer (CRC) stood witnessed as the major cause of deaths worldwide, especially in men. But the early detection and removal of polyps can reduce the death rate to a considerable extent. The various stages of colon cancer using the YOLOv3 Multi-Scale Framework (YOLOv3-MSF) have been studied here in this context. Colonoscopy is an examination method to detect changes in the rectum by inserting a long flexible tube into the rectum. For screening colon cancer, gastroenterologist often refers to the pathological images of colonoscopy for diagnosis. In recent decades, various machine learning algorithms are used for diagnosis to get practical and reliable results. More often, the stages of colon cancer can be inspected by the Tumor Length (TL). In this study, the polyps are segmented and allowed to measure the TL value by assessing polyp length (Plen) and polyp width (Pwid). The various stages of colon cancer have been ascertained based on the maximum length between the Plen and Pwid. Our research uses the single-stage YOLOv3 object detection model to locate and classify the various stages based on the TL value. It outputs the class probability and position coordinates of each image by enhancing accuracy and robustness. At the outset, during preprocessing to address the specific problems like reduced contrast, blurred images and n