CHRONIC KIDNEY DISEASE DETECTION AND RENAL ULTRASOUND IMAGE SEGMENTATION BASED ON MBFRCNN

In view of the current time-consuming and labor-intensive diagnosis of chronic kidney disease, the difficulty of manual diagnosis, and the low accuracy of recognition and segmentation, an instance segmentation model MBF RCNN, which can combine the glomerular filtration rate index and the comprehensive analysis of image texture features, is proposed to improve the accuracy of detection and segmentation in chronic kidney disease staging. Aimed at the low recognition accuracy of the baseline network, a feature fusion module was proposed, which adaptively fused multi-scale feature layers, reduced the inconsistency of feature maps at different levels, and improved the detection accuracy. For the blurred segmentation boundary, a self-attention mechanism was incorporated to fully learn the spatial features of the image. A boundary refinement module was added to further optimize the segmentation boundary. The model used a new combined loss function to improve the accuracy of the model. A chronic kidney disease dataset was established, and we compared this model with other state-of-the-art instance segmentation models on this dataset. It is about 3 percentage points higher than the baseline network Mask RCNN on mAP and 3. 46 percentage points higher than the baseline in segmentation accuracy.