DCNAS-Net: deformation convolution and neural architecture search detection network for bone marrow oedema

Abstract

Abstract Background Lumbago is a global disease that affects more than 500 million people worldwide. Bone marrow oedema is one of the main causes of the condition and clinical diagnosis is mainly made by radiologists manually reviewing MRI images to determine whether oedema is present. However, the number of patients with Lumbago has risen dramatically in recent years, which has brought a huge workload to radiologists. In order to improve the efficiency of diagnosis, this paper is devoted to developing and evaluating a neural network for detecting bone marrow edema in MRI images. Related work Inspired by the development of deep learning and image processing techniques, we design a deep learning detection algorithm specifically for the detection of bone marrow oedema from lumbar MRI images. We introduce deformable convolution, feature pyramid networks and neural architecture search modules, and redesign the existing neural networks. We explain in detail the construction of the network and illustrate the setting of the network hyperparameters. Results and discussion The detection accuracy of our algorithm is excellent. And its accuracy of detecting bone marrow oedema reached up to 90.6 $$\%$$ % , an improvement of 5.7 $$\%$$ % compared to the original. The recall of our neural network is 95.1 $$\%$$ % , and the F1-measure also reaches 92.8 $$\%$$ % . And our algorithm is fast in detecting it, taking only 0.144 s per image. Conclusion Extensive experiments have demonstrated that deformable convolution and aggregated feature pyramid structures are conducive for the detection of bone marrow oedema. Our algorithm has better detection accuracy and good detection speed compared to other algorithms.

Keywords

• Lumbago MRI
• Bone marrow oedema
• Neural networks
• Target detection