For six-class classification, the mean diagnostic accuracy was 97.3% (95% confidence interval, 96.0–98.6%) by DenseNet-161 and 95.9% (95% CI 94.1–97.7%) by EfficientNet-B7. A total of 1865 images were included from 703 patients, of which 10% were used as a test dataset. Digital photographs were taken of each pathological slide to fine-tune two pre-trained convolutional neural networks, and the model performances were evaluated. Histopathological slides of colonoscopic biopsy or resection specimens were collected and grouped into six classes by disease category: adenocarcinoma, tubular adenoma (TA), traditional serrated adenoma (TSA), sessile serrated adenoma (SSA), hyperplastic polyp (HP), and non-specific lesions. This study aimed to develop and validate deep learning models that automatically classify digital pathology images of colon lesions obtained from colonoscopy-related specimen. Colonoscopy is an effective tool to detect colorectal lesions and needs the support of pathological diagnosis.
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