An Analysis of YOLOvX Deep Learning Models for Colon Cancer Detection

Around the world, Colorectal cancer is still a prevalent form of cancer, and effective treatment and its impact depends on early diagnosis. Recently, polyp detection methods employ a convolutional neural networks to identify precancerous or malignant polyps in colonoscopy images accurately with high speed and precision. This study evaluates four deep learning models namely YOLOv3n, YOLOv5s, YOLOv7, and YOLOv7x, to determine their effectiveness in detecting colorectal cancer polyp frames efficiently. Among these, YOLOv7x model exhibited outstanding performance on the Hyper Kavasir dataset compared to other models, achieving an F1 score of 88.0%, a recall of 86.4%, a precision of 89.5%, and a mAP of 92.0% at a confidence threshold of 0.353. The achieved results highlight the unique capability of models in detecting CRC polyps for diagnosis. Incorporating such detection models can help physicians significantly in clinical practice to improve their ability to identify malignant polyps, ultimately leading to better treatment for patients. Gastroenterologists can benefit considerably from these models as diagnostic tools because of their speed and accuracy.