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Improved heterogeneous data fusion and multi‐scale feature selection method for lung cancer subtype classification

The diagnosis of the disease requires a variety of data and indicators. In order to make the computer perform intelligent computation and diagnosis from a doctor's perspective, complementary information between different modal data needs to be taken into account. Meanwhile, the redundancy features with key information should be selected in order to reduce the complexity of calculation. In this study, an adaptive dynamic loss function is proposed to weight different scales in the multi‐scale expansion network of pathological images according to the doctor's diagnosis process. And an ant colony algorithm based on maximum information coefficient correlation was designed for unsupervised feature selection of fusion features combined image feature and patient differential genes. Experimental results show that the addition of pathological image information and genetic information plays an important role in the classification of lung cancer subtypes. Compared with other feature selection methods, the proposed algorithm can quickly converge. Combining pathological image and gene expression matrix for cancer diagnosis can improve the diagnostic accuracy of specific patients, with an accuracy of 95.62 and AUC achieves 0.897. The proposed method has high effectiveness and superior performance in the classification of lung cancer.