The investigation of constraints in implementing robust AI colorectal polyp detection for sustainable healthcare system

Abstract

Colorectal cancer (CRC) is one of the significant threats to public health and the sustainable healthcare system during urbanization. As the primary method of screening, colonoscopy can effectively detect polyps before they evolve into cancerous growths. However, the current visual inspection by endoscopists is insufficient in providing consistently reliable polyp detection for colonoscopy videos and images in CRC screening. Artificial Intelligent (AI) based object detection is considered as a potent solution to overcome visual inspection limitations and mitigate human errors in colonoscopy. This study implemented a YOLOv5 object detection model to investigate the performance of mainstream one-stage approaches in colorectal polyp detection. Meanwhile, a variety of training datasets and model structure configurations are employed to identify the determinative factors in practical applications. The designed experiments show that the model yields acceptable results assisted by transfer learning, and highlight that the primary constraint in implementing deep learning polyp detection comes from the scarcity of training data. The model performance was improved by 15.6% in terms of average precision (AP) when the original training dataset was expanded. Furthermore, the experimental results were analysed from a clinical perspective to identify potential causes of false positives. Besides, the quality management framework is proposed for future dataset preparation and model development in AI-driven polyp detection tasks for smart healthcare solutions.

Sugarcane stem node identification algorithm based on improved YOLOv5

“…YOLOv5 is an improved one-stage target-detection algorithm based on YOLOv3 [26]. Compared with other algorithms in the field of target detection, YOLOv5 has the characteristics of a small model size, fast training and reasoning speed, and flexible use; thus, has been widely used in various fields [27][28][29]. Fig 4 shows the network structure of YOLOv5.…”

Intraoperative near infrared functional imaging of rectal cancer using artificial intelligence methods - now and near future state of the art

“…Once data is extracted, noise and artefact removed, and curve smoothing learning detection algorithms such as You Only Look Once (YOLO) detection and region based convoluted neural networks (R-CNN). This contrasts to the simpler machine learning classification methods we utilise [88,89], which have specific advantages as discussed below. Similar computational methods as our cancer classification method are now also in development for grounded bowel perfusion assessment.…”