A Lung Image Deep Learning Detection Model Based on Cross Residual Attention and Multi-feature Fusion

Abstract

Deep learning has become one of the hottest topics in medical image processing due to the development of deep learning technology. Currently, medical image research and applications suffer from two problems: a lack of data sets and an imbalance of classification categories. To solve these problems, we propose a method of residual attention and multi-feature fusion for lung image detection. Firstly, to integrate micro- and macro-feature extraction for medical image processing, two independent residual fusion strategies are designed, namely the Cross Residual Feature Extraction module (CRFE) and the Residual Attention Module (RAM). Secondly, a three-channel mechanism is designed for the Image Compensation Model (IFM). Using three channels and two residual fusion strategies, a multi-composite fusion architecture is produced to improve classifier performance. Finally, experimental results demonstrate that the proposed model performs better than the latest algorithms when compared with other medical image compensation methods.