Celiac disease diagnosis from endoscopic images based on multi-scale adaptive hybrid architecture model.
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Abstract

Celiac disease has emerged as a significant global public health concern, exhibiting an estimated worldwide prevalence of approximately 1%. However, existing research pertaining to domestic occurrences of celiac disease is confined mainly to case reports and limited case analyses. Furthermore, there is a substantial population of undiagnosed patients in the Xinjiang region.
Purpose:
This study endeavors to develop a novel and focused deep learning model. The primary objective is to facilitate celiac disease diagnosis through the utilization of a dataset comprising endoscopic images obtained from patients with celiac disease in Xinjiang.
Methods:
We propose a novel CNN-Transformer hybrid architecture for deep learning, tailored to the diagnosis of celiac disease using endoscopic images. Within this architecture, a multi-scale spatial adaptive selective kernel convolution feature attention module demonstrates remarkable efficacy in diagnosing celiac disease. We dynamically capture salient features within the local channel feature map that correspond to distinct manifestations of endoscopic image lesions in the celiac disease-affected areas such as the duodenal bulb, duodenal descending segment, and terminal ileum. This process serves to extract and fortify the spatial information specific to different lesions. This strategic approach facilitates not only the extraction of diverse lesion characteristics but also the attentive consideration of their spatial distribution. Additionally, we integrate the global representation of the feature map obtained from the Transformer with the locally extracted information via convolutional layers. This integration achieves a harmonious synergy that optimizes the diagnostic prowess of the model.
Results:
Overall, the accuracy, specificity, F1-Score, and precision in the experimental results were 98.38%, 99.04%, 98.66% and 99.38%, respectively.
Conclusion:
This study introduces a deep learning network equipped with both global feature response and local feature extraction capabilities. This innovative architecture holds significant promise for the accurate diagnosis of celiac disease by leveraging endoscopic images captured from diverse anatomical sites.
.© 2024 Institute of Physics and Engineering in Medicine.