EBHI:A New Enteroscope Biopsy Histopathological H&E Image Dataset for Image Classification Evaluation

Abstract

Background and purpose: Colorectal cancer has become the third most common cancer worldwide, accounting for approximately 10% of cancer patients. Early detection of the disease is important for the treatment of colorectal cancer patients. Histopathological examination is the gold standard for screening colorectal cancer. However, the current lack of histopathological image datasets of colorectal cancer, especially enteroscope biopsies, hinders the accurate evaluation of computer-aided diagnosis techniques. Methods: A new publicly available Enteroscope Biopsy Histopathological H&E Image Dataset (EBHI) is published in this paper. To demonstrate the effectiveness of the EBHI dataset, we have utilized several machine learning, convolutional neural networks and novel transformer-based classifiers for experimentation and evaluation, using an image with a magnification of 200×. Results: Experimental results show that the deep learning method performs well on the EBHI dataset. Traditional machine learning methods achieve maximum accuracy of 76.02% and deep learning method achieves a maximum accuracy of 95.37%. Conclusion: To the best of our knowledge, EBHI is the first publicly available colorectal histopathology enteroscope biopsy dataset with four magnifications and five types of images of tumor differentiation stages, totaling 5532 images. We believe that EBHI could attract researchers to explore new classification algorithms for the automated diagnosis of colorectal cancer, which could help physicians and patients in clinical settings.

Classification of Colorectal Polyps from Histopathological Images using Ensemble of ConvNeXt Variants

“…Moreover, the WSIs belong to the following classes; normal tissue, hyperplastic polyp, tubular adenoma and tubulo-villous adenoma [29]. EBHI is composed of 5532 WSIs which has the following categories, normal, low-grade and high-grade intraepithelial neoplasm, and adenocarcinoma, divided into four magnifications of 40×, 100×, 200× and 400× [30].…”

“…• In this study, we explored state-of-the-art pre-trained Deep CNN algorithms' performances on our custom dataset. In order to comprehensively evaluate and assess the generalizability of the proposed model, during the testing phase, we also employ publicly available UniToPatho and EBHI databases [29], [30]. The proposed ensemble model achieves an accuracy of 95% on our custom dataset.…”