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
Automatic segmentation of targets from medical images is a difficult task because of the high complexity of medical images and the lack of simple linear features. U-Net is widely used in the field of medical image segmentation for its state-of-the-art performance. However, there are still many problems, the most serious of which are the lack of accuracy of image segmentation and the large amount of information loss. To alleviate these deficiencies, we propose a UNetbased image segmentation model GAM-UNet, which has the same coder-decoder structure. The Global Attention Mechanism (GAM) is introduced and embedded into the encoding part of the proposed model to automatically adjust the weights of the feature maps. Evaluation results on the public datasets Data Science Bowl and CVC-ClinicDB show that our model performs better than other U-Net variants. After additional analysis, our proposed model is more hardware friendly and allows for faster medical diagnosis with a slight increase in accuracy. Our proposed approach can also be extended to other image processing tasks, such as object detection and tracking.
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