Abstract:
Colorectal cancer (CRC) is the world’s third most common cancer, with the second high est fatality rate. It is primarily the result of lower gastrointestinal tract (GI) disorders. The
prevention of CRC mainly depends on the early detection and treatment of anomalies in
the lower GI tract. Colonoscopy is the gold standard device used for diagnosing abnormal ities in the lower GI tract as well as identifying anatomical landmarks and bowel prepara tion scales. However, it is time-consuming, tedious, and prone to error process, especially
for those hospitals in low resource settings. Therefore, in this research, a real-time auto mated detection, classification, and localization of lower GI tract pre-colorectal cancerous
abnormalities were done. The proposed system enables real-time detection, classification,
and localization of common pathology, anatomical landmarks, and bowel preparation scale
from colonoscopy images. To do the research, data was gathered both online (at hyper k vasir dataset) and locally from the Yanet Internal Specialized Center and the Ethio-Tebib
Hospital. Data augmentation techniques were applied to increase the training dataset. The
pre-trained transfer learning SSD, YOLOv4, and YOLOv5 object detection model was used
to develop the system with minimal fine-tuning of the hyper parameters and their perfor mance was compared. The Yolo v5 model achieves good precision, recall, and mean aver age precision (mAP), 99.071%, 98.064% and 98.8%, respectively, on the testing data set. The
developed artificial intelligence-based module would have the potential to assist gastroen terologists and general practitioners in decision-making. Even though the proposed work
achieved the best performance, further improvement is required by increasing the size of
the dataset to include other GI tract disease diagnoses.
