IEEE Access (Jan 2024)
Neural Network-Based Pipeline With High-Resolution Feature Enhancement and Low-Resolution Feature Preservation for Automated Treatment Decision of Graves’ Orbitopathy Patients
Abstract
Graves’ orbitopathy is an inflammatory disorder that causes changes in different structures close to the eye. Accurate and consistent diagnoses are essential to improve the quality of life for Graves’ orbitopathy patients. To this end, a number of studies on Graves’ orbitopathy have been conducted based on neural networks recently. However, treatment decision methods based on neural networks have been much less addressed. This study aims to propose an effective deep neural network-based diagnosis method that makes treatment decisions for Graves’ orbitopathy patients. Specifically, the proposed method adopts a high-resolution feature enhancement and low-resolution feature preservation strategy focusing on the following points. First, the loss of detailed spatial information during the alignment of pixel spacing in computed tomography images leads to a decrease in performance. Thus, we preserve the detailed information of the images through high-resolution resampling. Second, existing studies lack sophistication in network design. The baseline network was improved by four modifications. Finally, resizing and coarse cropping cause learning instability. Thus, we adopt padding and fine-grained cropping. Our empirical study shows that the proposed method outperforms two existing neural network-based Graves’ orbitopathy diagnostic pipelines achieving an average area under the receiver operating characteristic curve of 0.793, accuracy of 0.699, F1 score of 0.416, sensitivity of 0.723, and specificity of 0.694 in five repetitive experiments. Furthermore, in-depth analysis provides several future research directions in computed tomography preprocessing and deep neural network design. The source code for the proposed model is available at https://github.com/tkdgur658/GOTDNet.
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