Application value of artificial intelligence algorithm-based magnetic resonance multi-sequence imaging in staging diagnosis of cervical cancer

Abstract

The aim of this research is to explore the application value of Deep residual network model (DRN) for deep learning-based multi-sequence magnetic resonance imaging (MRI) in the staging diagnosis of cervical cancer (CC). This research included 90 patients diagnosed with CC between August 2019 and May 2021 at the hospital. After undergoing MRI examination, the clinical staging and surgical pathological staging of patients were conducted. The research then evaluated the results of clinical staging and MRI staging to assess their diagnostic accuracy and correlation. In the staging diagnosis of CC, the feature enhancement layer was added to the DRN model, and the MRI imaging features of CC were used to enhance the image information. The precision, specificity, and sensitivity of the constructed model were analyzed, and then the accuracy of clinical diagnosis staging and MRI staging were compared. As the model constructed DRN in this research was compared with convolutional neural network (CNN) and the classic deep neural network visual geometry group (VGG), the precision was 67.7, 84.9, and 93.6%, respectively. The sensitivity was 70.4, 82.5, and 91.2%, while the specificity was 68.5, 83.8, and 92.2%, respectively. The precision, sensitivity, and specificity of the model were remarkably higher than those of CNN and VGG models (P < 0.05). As the clinical staging and MRI staging of CC were compared, the diagnostic accuracy of MRI was 100%, while that of clinical diagnosis was 83.7%, showing a significant difference between them (P < 0.05). Multi-sequence MRI under intelligent algorithm had a high diagnostic rate for CC staging, deserving a good clinical application value.

Keywords: artificial intelligence algorithm; cervical cancer staging; feature extraction; magnetic resonance multi-sequences.

Figure 1

Structure diagram of DRN.

Figure 2

Flow chart of CC image preprocessing.

Figure 3

Comparison of different network accuracy rates. (Note: * suggests a great difference with P < 005 for comparison between CNN and VGG).

Figure 4

Comparison of weight initialization methods. Ⅰ, Ⅱ, Ⅲ, and Ⅳ were zeroing, randomization, reference [22], and reference [23], respectively.

Figure 5

Comparison of model classification results. (a) Precision comparison; (b) sensitivity comparison; and (c) specificity comparison. * Compared with that of CNN and VGG, P < 0.05.

Figure 6

MRI images. (a) Parametrial mass infiltration; (b) enhanced MRI image of the cervix; and (c) parametrial MRI image suggesting high signals on T2WI.

Figure 7

Comparison of MRI images before and after feature enhancement. (a) Original feature and (b) processed feature.

Figure 8

Results of diagnostic evaluation. * Indicated that there was a significant difference between MRI and clinical staging, P < 0.05.