Highlights
- •EBHI can be applied to verify performance of existing classification methods.
- •This paper verifies the accuracy from linear regression to Transformer on EBHI.
- •Classification results by different methods are analysed on EBHI.
- •EBHI is very distinguishable in the results of different classification methods.
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. Therefore, a multi-category
colorectal cancer dataset is needed to test various medical image classification methods
to find high classification accuracy and strong robustness.
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. Classical 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.
Keywords
To read this article in full you will need to make a payment
Purchase one-time access:
Academic & Personal: 24 hour online accessCorporate R&D Professionals: 24 hour online accessOne-time access price info
- For academic or personal research use, select 'Academic and Personal'
- For corporate R&D use, select 'Corporate R&D Professionals'
Subscribe:
Subscribe to Physica Medica: European Journal of Medical PhysicsAlready a print subscriber? Claim online access
Already an online subscriber? Sign in
Register: Create an account
Institutional Access: Sign in to ScienceDirect
References
- Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries.CA: Cancer J Clin. 2021; : 1-41https://doi.org/10.3322/caac.21660
- Advances in endoscopy for colorectal polyp detection and classification.in: Baylor university medical center proceedings, vol. 33, no. 1. Taylor & Francis, 2020: 28-35
- CVM-Cervix: A hybrid cervical pap-smear image classification framework using CNN, visual transformer and multilayer perceptron.Pattern Recognit. 2022; 108829
- Early colon cancer: ESMO clinical practice guidelines for diagnosis, treatment and follow-up.Ann Oncol. 2013; 24: vi64-vi72
- The wonderful colors of the hematoxylin–eosin stain in diagnostic surgical pathology.Int J Surg Pathol. 2014; 22: 12-32
- Robbins basic pathology e-book.Elsevier Health Sciences, 2017
- Breast cancer detection from histopathology images using modified residual neural networks.Biocybern Biomed Eng. 2021; 41: 1272-1287
- Artificial intelligence applied to medicine: There is an “elephant in the room”.Phys Med Eur J Med Phys. 2022; 98: 8-10
- Multi-class texture analysis in colorectal cancer histology.Sci Rep. 2016; 6: 1-11
- Collection of textures in colorectal cancer histology.Zenodo, 2016https://doi.org/10.5281/zenodo.53169
- 100,000 histological images of human colorectal cancer and healthy tissue.Zenodo, 2018https://doi.org/10.5281/zenodo.1214456
- Gland segmentation in colon histology images: The glas challenge contest.Med Image Anal. 2017; 35: 489-502
Wei J, Suriawinata A, Ren B, Liu X, Lisovsky M, Vaickus L, et al. Learn like a pathologist: curriculum learning by annotator agreement for histopathology image classification. In: Proceedings of the IEEE/CVF winter conference on applications of computer vision. 2021, p. 2473–83.
- CAD systems for colorectal cancer from WSI are still not ready for clinical acceptance.Sci Rep. 2021; 11: 1-15
- AI based pre-screening of large bowel cancer via weakly supervised learning of colorectal biopsy histology images.2022 (medRxiv)
- Basic of machine learning and deep learning in imaging for medical physicists.Phys Medica. 2021; 83: 194-205
- AI applications to medical images: From machine learning to deep learning.Phys Medica. 2021; 83: 9-24
- Data preparation for artificial intelligence in medical imaging: A comprehensive guide to open-access platforms and tools.Phys Medica. 2021; 83: 25-37
- Current challenges of implementing artificial intelligence in medical imaging.Phys Medica. 2022; 100: 12-17
- SVIA dataset: A new dataset of microscopic videos and images for computer-aided sperm analysis.Biocybern Biomed Eng. 2022; 42: 204-214
- A state-of-the-art survey of object detection techniques in microorganism image analysis: from classical methods to deep learning approaches.Artif Intell Rev. 2022; : 1-72
- Fine-tuning and training of DenseNet for histopathology image representation using TCGA diagnostic slides.2021 (arXiv e-prints, arXiv–2101)
- A neural pathomics framework for classifying colorectal cancer histopathology images based on wavelet multi-scale texture analysis.Sci Rep. 2021; 11: 1-10
- IL-MCAM: An interactive learning and multi-channel attention mechanism-based weakly supervised colorectal histopathology image classification approach.Comput Biol Med. 2022; 143105265
- SA-GAN: Stain acclimation generative adversarial network for histopathology image analysis.Appl Sci. 2021; 12: 288
- A comparative study of deep learning classification methods on a small environmental microorganism image dataset (EMDS-6): from convolutional neural networks to visual transformers.Front Microbiol. 2022; 13
- Pathology of colorectal cancer.Dig Liver Dis. 2001; 33: 372-388
- Pathology of the malignant colorectal polyp.Hum Pathol. 1998; 29: 15-26
- An application of transfer learning and ensemble learning techniques for cervical histopathology image classification.IEEE Access. 2020; 8: 104603-104618
- Missed diagnosis of early gastric cancer or high-grade intraepithelial neoplasia.World J Gastroenterol: WJG. 2013; 19: 2092
- A comprehensive review for breast histopathology image analysis using classical and deep neural networks.IEEE Access. 2020; 8: 90931-90956
- Histological typing of intestinal tumours.Springer Science & Business Media, 2012
- GasHisSDB: A new gastric histopathology image dataset for computer aided diagnosis of gastric cancer.Comput Biol Med. 2022; 142105207
- Histogram of oriented gradient-based fusion of features for human action recognition in action video sequences.Sensors. 2020; 20: 7299
- Multi-level fusion in ultrasound for cancer detection based on uniform LBP features.Comput Mater Continua. 2021; 66: 3363-3382
- Feature extraction and selection of kidney ultrasound images using GLCM and PCA.Procedia Comput Sci. 2020; 167: 1722-1731
- A hierarchical conditional random field-based attention mechanism approach for gastric histopathology image classification.Appl Intell. 2022; : 1-22
- Gastric histopathology image segmentation using a hierarchical conditional random field.Biocybern Biomed Eng. 2020; 40: 1535-1555
- CoMHisP: A novel feature extractor for histopathological image classification based on fuzzy SVM with within-class relative density.IEEE Trans Fuzzy Syst. 2020; 29: 103-117
- Linear regression analysis study.J Pract Cardiovasc Sci. 2018; 4: 33
- A generalized mean distance-based k-nearest neighbor classifier.Expert Syst Appl. 2019; 115: 356-372
- A novel collaborative filtering model for LncRNA-disease association prediction based on the Naïve Bayesian classifier.BMC Bioinformatics. 2019; 20: 1-13
- Improved random forest for classification.IEEE Trans Image Process. 2018; 27: 4012-4024
- A support vector machine-based ensemble algorithm for breast cancer diagnosis.European J Oper Res. 2018; 267: 687-699
- Predicting liver patients using artificial neural network.Int J Acad Inf Syst Res (IJAISR). 2019; 3
- MetaMed: Few-shot medical image classification using gradient-based meta-learning.Pattern Recognit. 2021; 120108111
- Very deep convolutional networks for large-scale image recognition.2014 (arXiv preprint arXiv:1409.1556)
Szegedy C, Vanhoucke V, Ioffe S, Shlens J, Wojna Z. Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2016, p. 2818–26.
He K, Zhang X, Ren S, Sun J. Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. 2016, p. 770–8.
- An image is worth 16x16 words: Transformers for image recognition at scale.2020 (arXiv preprint arXiv:2010.11929)
- Gashis-transformer: A multi-scale visual transformer approach for gastric histopathology image classification.2021 (arXiv preprint arXiv:2104.14528)
- MobiHisNet: a lightweight CNN in mobile edge computing for histopathological image classification.IEEE Internet Things J. 2021; 8: 17778-17789
- MediSecFed: Private and secure medical image classification in the presence of malicious clients.IEEE Trans Ind Inf. 2021; 18: 5648-5657
- Deep feature learning for histopathological image classification of canine mammary tumors and human breast cancer.Inform Sci. 2020; 508: 405-421
- Identification of COVID-19 samples from chest X-Ray images using deep learning: A comparison of transfer learning approaches.J X-Ray Sci Technol. 2020; 28: 821-839
- A comprehensive review of computer-aided whole-slide image analysis: from datasets to feature extraction, segmentation, classification and detection approaches.Artif Intell Rev. 2022; : 1-70
Article info
Publication history
Published online: February 17, 2023
Accepted:
January 25,
2023
Received in revised form:
August 30,
2022
Received:
April 7,
2022
Identification
Copyright
© 2023 Associazione Italiana di Fisica Medica e Sanitaria. Published by Elsevier Ltd. All rights reserved.
