Medical image recognition system and method based on multi-modal fusion
A medical image and recognition system technology, applied in the field of medical image detection, can solve the problems of inability to integrate various medical images, low recognition accuracy, poor detection comprehensiveness, etc., to improve recognition stability and accuracy, and high recognition accuracy , The effect of improving the generalization ability
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Embodiment 1
[0091] A medical image recognition system based on multimodal fusion, including
[0092] Mammogram recognition model, used to construct 2D-CNN to obtain mammogram features and complete mammogram recognition;
[0093] B-ultrasound video sequence recognition model, used to construct CNN and LSTM to obtain B-ultrasound video sequence features, and complete B-ultrasound video sequence recognition;
[0094] MRI sequence recognition model, used to construct 3D-CNN to obtain MRI sequence features and complete MRI sequence recognition;
[0095] The multimodal decision-making fusion unit is used for decision-making fusion of mammogram recognition results, B-ultrasound video sequence recognition results and MRI sequence recognition results through multiplication to obtain the final recognition result.
[0096] A medical image recognition method based on multimodal fusion, comprising the steps of:
[0097] Step 1: Label and classify the collected mammograms, B-ultrasound images and MRI...
Embodiment 2
[0102] Based on embodiment 1, first construct the recognition network of corresponding image, and it is trained to obtain mammogram recognition model, B supersonic video sequence recognition model and MRI sequence recognition model;
[0103] In step 2, the mammogram recognition model training includes the following steps:
[0104] Step a1: Build a mammogram recognition model, which includes sequentially connected 2D-CNN, fully connected layer and Softmax;
[0105] Step a2: Input the training set in the step 1 classified image into the mammogram recognition model to train at intervals, and record the model training parameters until the objective function curve no longer declines;
[0106] Step a3: Input the verification set in the classification image into the mammogram recognition model in step a2 for testing, and record six models with good recognition effect;
[0107] Step a4: Input the test set data into the six models mentioned in step a3, and select the one with the high...
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