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

TL;DR

This paper introduces EBHI, a comprehensive public dataset of colorectal histopathological images from enteroscope biopsies, enabling improved evaluation of machine learning methods for early colorectal cancer detection.

Contribution

The paper presents the first publicly available colorectal histopathology dataset with multiple magnifications and tumor stages, facilitating research in automated diagnosis.

Findings

Deep learning methods achieved up to 95.37% accuracy.

Traditional machine learning methods reached 76.02% accuracy.

EBHI dataset includes 5532 images across five tumor differentiation stages.

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 200x. 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.