TITLE: 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 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.
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.
EBHI:A New Enteroscope Biopsy Histopathological H&E Image Dataset for Image Classification Evaluation
http://arxiv.org/abs/2202.08552v1