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Lesion detection method based on double modules for feature extraction

In the lesion detection, deep learning mainly identifies true lesion area from the Computed Tomography (CT) scans. However, the small lesion area is hard to be detected by deep learning due to the similar texture characteristics between the target area and the background, and the small proportion of the lesion area in the medical images. Thus, this paper proposes a lesion detection method based on double modules for feature extraction (LDDM-FE). A feature extraction method based on double modules combines the attention module and global context module in the feature extraction network to identify the small lesion area from the medical images. A multi-level feature fusion method forms the feature maps based on the two modules to improve the detection accuracy of small lesion areas. The proposed method is compared with some related lesion detection methods on some DeepLesion datasets. The results demonstrate that this method significantly improves the detection accuracy of multiple abdominal diseases, and effectively improves the efficiency of lesion detection.