Application of YOLO models for colorectal cancer diagnosis: A case study in Vietnam

Abstract

Early detection and treatment of colorectal cancer (CRC) are expected to increase the survival rate of patients. Deep learning methods for automatically diagnosing diseases based on CT images are increasingly developed, and good data preparation plays an important role in affecting the effectiveness of the training model. This study will introduce how we collect and process data for the object detection model, evaluate the effectiveness of the data based on the performance evaluation of the YOLOv8m, YOLOv9c, YOLOv10s models, and evaluate the effectiveness of disease sign detection on authentic images collected at hospitals that have never been used to train the model. We collected public data on the TCIA website (1558 slices showing CRC lesions, 121 patients) and actual data collected at Can Tho City Oncology Hospital, Vietnam (19093 JPEG CT images of 31 patients). As a result, the training and external evaluation models all achieved high results (YOLOv8m: The F1 score, recall, and mAP were all stable at 95% or higher; YOLOv9c: Precision = 1, F1 score = 97. 4%, recall = 95%, mAP = 94.9%; YOLOv10s: F1 score = 97. 4%, mAP = 94. 4%, Recall = 95%). When evaluated on an external dataset collected from Can Tho Oncology Hospital, the results showed that the trained models could detect signs of disease in 10/10 patients with a confidence level of 50% or higher.

Conflict of Interest

The authors have no relevant financial or non-financial interests to disclose.