This study aimed to explore the evaluation of Adriamycin-loaded microspheres in the treatment of liver cancer under DenseNet-based magnetic resonance imaging (MRI) image classification algorithm. According to different treatment methods, the research objects were classified into a normal saline (saline) group, a doxorubicin raw material (DOX) group, and a chitosan cross-linked pectin-doxorubicin conjugate macromolecular (CS-PDC-M) group. DenseNet’s migration learning was employed to analyze the dynamic enhanced MRI characteristics and classify the MRI images. The CS-PDC-M-targeted nanotransfer system was examined with its apparent morphology, drug absorption, and cytotoxicity. Tumor volume was monitored using MRI, and alanine aminotransferase (ALT) and creatine kinase isoenzyme (CK-MB) values were detected. Results showed that the classification accuracy of liver cancer MRI image based on DenseNet model reached 80% at the arterial hepatobiliary stage. The DOX and CS-PDC-M group had obviously smaller tumor volume than that of the saline group
P
<
0.05
with a statistical meaning. The mortality in the DOX group was 30%, while there was no death in the saline and CS-PDC-M groups. Compared with the saline and CS-PDC-M groups, ALT and CK-MB from the DOX group increased substantially
P
<
0.05
. Therefore, DOX had an inhibitory effect on tumor but damaged the heart and liver. DOX was used to construct CS-PDC-M that could maintain the original treatment effect of DOX and inhibit its side effects on the body, so CS-PDC-M had a clinical application value. In conclusion, Adriamycin-loaded microspheres could not only maintain the original therapeutic effect of Adriamycin but also inhibit its toxic and side effects on the body. The DenseNet model was applied in the liver cancer MRI dynamic image classification algorithm, and the normalization algorithm could improve the accuracy of the liver cancer microvessel classification, thus promoting the diagnostic efficiency of liver cancer diagnosis, which had clinical application value.
DenseNet-Based Classification of MRI Images for Detecting the Difference before and after Treating Liver Cancer