Similar to the critical point tracking technology of human vision, the split attentionspatial grouping enhancement module, combining the multi-path grouping architecturespatial attention technology, can accurately extract important information from the imageimprove the network performance. Moreover, adopting neural architecture search technology to automatically search for the most suitable network architecture based on current moduleshardware devices can balance diagnosis speedhigh accuracy.

The RL model develops a control policy directly from experience to predict statesrewards during a learning procedure. Hence, we designed a medical image environment including US images, different actions,rewards, agent learns in this environment to extract the ALN regionevaluates the status. The performance of our proposed method achieves an accuracy of 83.6, a sensitivity of 88.6,a specificity of 89.0.

Our technique is a liver tumor detection method using machine learning which is the most popular AI technique in these years. the high-magnification image provides more details, but the view is smaller in contrast, a low-magnification image has the larger view but less details. Thus, our method combines the two magnificationsyields more accurate tumor detection results.