Analysis of Hyperspectral Imaging Using CNN-GRU for Gastric Adenomatous Polyp and Adenocarcinoma Classification

Early identification of gastric adenomatous polyps and adenocarcinoma is vital for improving patient outcomes. This study proposes a hybrid CNN-GRU model to classify one-dimensional hyperspectral data from ex vivo gastric tissues, addressing limitations of traditional diagnostics. Our model innovatively combines convolutional neural networks (CNNs) and gated recurrent units (GRUs) to capture both spatial and sequential dependencies in spectral data. Experimental results demonstrate that our model achieves an accuracy of 86%, sensitivity of 88%, and specificity of 85%. Additionally, receiver operating characteristic analysis further underscores its robust performance with an area under the curve of 0.86, surpassing traditional methods and other baseline models. These findings highlight the potential of leveraging advanced machine learning techniques to enhance early diagnostic accuracy and treatment strategies. The proposed approach offers a promising tool for rapid, accurate differentiation of gastric lesions, underscoring the importance of integrating innovative technologies in clinical diagnostics.