Deformation and Refined Features Based Lesion Detection on Chest X-Ray

Automatic and accurate detection of chest X-ray lesion is a challenging task. In the chest X-ray image, the lesions are shown with blurred boundary contours, different sizes, variable shapes, uneven density, etc. Besides, the deep convolutional neural network (CNN) consists of traditional convolution units, which has the limitations of rectangular sampling. The CNN extracts difficultly the deformation and refined features of chest X-ray lesions. Because of these factors, the accuracy of the lesion detection algorithm is not high. To deal with problems, we propose the deformation and refined features based lesion detection on the chest X-ray algorithm called DRCXNet. Firstly, the deformable convolution with amplitude modulation (AMDCN) is built to extract the deformation features of the lesions on the chest X-ray. Secondly, to obtain the refined feature, the global features and local features are fused, which can enrich the feature space of the lesion. Thirdly, the pooling layer combines with the AMDCN and region proposal network to establish the deformable pooling layer, which enhances the model’s sensitivity to the lesion location. During the training, the model is optimized by the improved regression loss function with a gradient control factor. On the public datasets RSNA and ChestX-ray8, the proposed method outperforms seven popular detection algorithms. The proposed method is a significant performance in both qualitative and quantitative experiments. Its comprehensive evaluation scores, sensitivity, precision, and the mean dice similarity coefficient are 0.866, 0.914, 0.836 and 0.859 respectively. The proposed algorithm achieves a very satisfactory result.