The invention relates to an NAC
curative effect prediction method and device based on multi-
modal feature fusion and Heman optimization, and the method comprises the steps: firstly, extracting
radiomics features, including texture, morphology, first-order statistics,
wavelet and other features, from ultrasonic images of three modalities of two-dimensional gray scale, elasticity and
color Doppler blood flow imaging, splicing the feature vectors, and carrying out the fusion of the feature vectors; a high-dimensional
feature vector is formed, and
lesion information is comprehensively represented. Then, an improved Hemma optimization
algorithm is adopted to carry out dimension reduction on the features, and penalty factors and kernel parameters of a
support vector machine are optimized. According to the
algorithm,
chaotic mapping is introduced, so that the global search capability is enhanced,
local optimum is avoided, and the optimal feature subset and SVM parameters are screened out. Finally, an SVM model is trained based on the optimized features and parameters, an independent
test set is used for model evaluation, and compared with various existing optimization algorithms, the advantages of the improved HO
algorithm in the aspect of improving
breast cancer NAC
curative effect prediction accuracy are verified.