|
Rectal cancer poses a huge threat to human health. To establish appropriate treatment strategies, precise preoperative staging is essential. Computed tomography (CT) has consistently played a crucial role in the preoperative examination of rectal cancer patients. However, the diagnostic abilities and efficiencies of radiologists in identifying rectal cancer T stages using CT images still need improvement. With the rapid advancement of deep learning (DL) technology, applying DL technology to CT image recognition can expedite the construction of an efficient image recognition platform, offering novel possibilities to tackle this issue. In this study, a novel ResFusNet model is proposed for rectal cancer T staging using CT images, showing efficient and accurate performance. For the ResFusNet model, the accuracy, precision, recall, F1 score and Matthews correlation coefficient (MCC) of the test set reached 99.89%, 99.94%, 99.84%, 99.88% and 99.85%, respectively. These results notably outperformed the performance of other models. In light of this, ResFusNet has the potential to become a highly sensitive model for rectal cancer T stage diagnosis and serve as a valuable auxiliary tool for clinical physicians
|
