| Title |
Application of Medical Intelligent Images Based on MTCNN and Attention Mechanism in Pathological Analysis |
| Authors |
(Junye Yang) ; (Yujuan Du) ; (Fang Liu) |
| DOI |
https://doi.org/10.5573/IEIESPC.2025.14.4.507 |
| Keywords |
Medical imaging; Cell nucleus segmentation; Deep learning; Interpretable algorithms |
| Abstract |
In response to the problem of excessive reliance on the experience and knowledge of doctors in the current field of medical image processing, research has proposed two new algorithms: cell nucleus segmentation algorithms based on multi-task cascaded convolutional networks and medical image interpretable algorithms based on attention mechanism deep convolutional residual networks. The experimental results showed that the average F1 Score, IoU, DICE1, DICE2, Jaccard composite index, and average accuracy of the multi task cascaded convolutional network are around 0.84, 0.85, 0.88, 0.75, 0.81, and 0.80, respectively, which are higher than those of the DCAN and Jbarker models. The average diagnostic conclusion prediction accuracy, average diagnostic conclusion prediction recall, average semantic attribute prediction accuracy, and average semantic attribute prediction recall of the attention mechanism deep convolutional residual network were approximately 85.2%, 89.3%, 76.5%, and 79.9%, respectively, which are higher than the AlexNet and ResNet algorithms. In addition, the average diagnostic conclusion and semantic attribute prediction accuracy of the two indicators of the attention mechanism deep convolutional residual network were about 86.1% and 79.5%, respectively, which are higher than other models. The outcomes of the study indicate that both algorithms proposed can effectively process medical images. |