Lung injury intelligent detection system based on lung image and pathological data

Abstract

The invention relates to the field of intelligent medicine, in particular to a lung injury intelligent detection system based on lung images and pathological data, which comprises a multi-modal data acquisition module, an intelligent segmentation module, a cross-modal feature fusion module and a lung injury classification detection module, a trans-attention multi-modal feature fusion method based on bidirectional gating is adopted, a lung CT image and a pathological text are coded, modal interaction is achieved through a bidirectional gating attention mechanism, a lesion area is highlighted, a channel-space attention gate is introduced, feature fusion is optimized, and accurate lung injury detection is achieved. A medical image segmentation method based on multi-layer fusion optimization and multi-layer parallel enhancement is adopted, through multi-layer feature optimization based on depth separable convolution and three-layer parallel feature enhancement based on a residual mechanism, key features, namely a focus area, are reserved, local-global features are effectively integrated while nonlinear information is captured, and the image segmentation efficiency is improved. The lung lesion area is accurately segmented, and the accuracy of lung injury detection is improved.