{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,23]],"date-time":"2026-10-23T23:48:37Z","timestamp":1792799317462,"version":"4.5.0"},"reference-count":41,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2021,3,9]],"date-time":"2021-03-09T00:00:00Z","timestamp":1615248000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100010790","name":"Erasmus+","doi-asserted-by":"publisher","award":["Programme, Key Action 1 \u2013 Student Mobility for Traineeship, 2019\/2020."],"award-info":[{"award-number":["Programme, Key Action 1 \u2013 Student Mobility for Traineeship, 2019\/2020."]}],"id":[{"id":"10.13039\/501100010790","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J. Imaging"],"abstract":"<jats:p>In recent years, automatic tissue phenotyping has attracted increasing interest in the Digital Pathology (DP) field. For Colorectal Cancer (CRC), tissue phenotyping can diagnose the cancer and differentiate between different cancer grades. The development of Whole Slide Images (WSIs) has provided the required data for creating automatic tissue phenotyping systems. In this paper, we study different hand-crafted feature-based and deep learning methods using two popular multi-classes CRC-tissue-type databases: Kather-CRC-2016 and CRC-TP. For the hand-crafted features, we use two texture descriptors (LPQ and BSIF) and their combination. In addition, two classifiers are used (SVM and NN) to classify the texture features into distinct CRC tissue types. For the deep learning methods, we evaluate four Convolutional Neural Network (CNN) architectures (ResNet-101, ResNeXt-50, Inception-v3, and DenseNet-161). Moreover, we propose two Ensemble CNN approaches: Mean-Ensemble-CNN and NN-Ensemble-CNN. The experimental results show that the proposed approaches outperformed the hand-crafted feature-based methods, CNN architectures and the state-of-the-art methods in both databases.<\/jats:p>","DOI":"10.3390\/jimaging7030051","type":"journal-article","created":{"date-parts":[[2021,3,9]],"date-time":"2021-03-09T04:33:51Z","timestamp":1615264431000},"page":"51","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":58,"title":["Two Ensemble-CNN Approaches for Colorectal Cancer Tissue Type Classification"],"prefix":"10.3390","volume":"7","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9473-044X","authenticated-orcid":false,"given":"Emanuela","family":"Paladini","sequence":"first","affiliation":[{"name":"Department of Innovation Engineering, University of Salento, 73100 Lecce, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0965-4919","authenticated-orcid":false,"given":"Edoardo","family":"Vantaggiato","sequence":"additional","affiliation":[{"name":"Department of Innovation Engineering, University of Salento, 73100 Lecce, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5077-4862","authenticated-orcid":false,"given":"Fares","family":"Bougourzi","sequence":"additional","affiliation":[{"name":"Univ. Polytechnique Hauts-de-France, Univ. Lille, CNRS, Centrale Lille, UMR 8520\u2014IEMN, F-59313 Valenciennes, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1073-2390","authenticated-orcid":false,"given":"Cosimo","family":"Distante","sequence":"additional","affiliation":[{"name":"Department of Innovation Engineering, University of Salento, 73100 Lecce, Italy"},{"name":"Institute of Applied Sciences and Intelligent Systems, National Research Council of Italy, 73100 Lecce, Italy"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9092-735X","authenticated-orcid":false,"given":"Abdenour","family":"Hadid","sequence":"additional","affiliation":[{"name":"Univ. Polytechnique Hauts-de-France, Univ. Lille, CNRS, Centrale Lille, UMR 8520\u2014IEMN, F-59313 Valenciennes, France"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7218-3799","authenticated-orcid":false,"given":"Abdelmalik","family":"Taleb-Ahmed","sequence":"additional","affiliation":[{"name":"Univ. Polytechnique Hauts-de-France, Univ. Lille, CNRS, Centrale Lille, UMR 8520\u2014IEMN, F-59313 Valenciennes, France"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2021,3,9]]},"reference":[{"key":"ref_1","unstructured":"Farahani, N., Parwani, A.V., and Pantanowitz, L. (2015). Whole slide imaging in pathology: Advantages, limitations, and emerging perspectives. Pathol. Lab. Med. Int., 7."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Pantanowitz, L., Sharma, A., Carter, A.B., Kurc, T., Sussman, A., and Saltz, J. (2018). Twenty years of digital pathology: An overview of the road travelled, what is on the horizon, and the emergence of vendor-neutral archives. J. Pathol. Inform., 9.","DOI":"10.4103\/jpi.jpi_69_18"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"884","DOI":"10.1016\/j.devcel.2010.05.012","article-title":"Tumors as organs: Complex tissues that interface with the entire organism","volume":"18","author":"Egeblad","year":"2010","journal-title":"Dev. Cell"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1093\/annonc\/mds246","article-title":"The proportion of tumor-stroma as a strong prognosticator for stage II and III colon cancer patients: Validation in the VICTOR trial","volume":"24","author":"Huijbers","year":"2013","journal-title":"Ann. Oncol."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"323","DOI":"10.1038\/nrc3261","article-title":"Intra-tumour heterogeneity: A looking glass for cancer?","volume":"12","author":"Marusyk","year":"2012","journal-title":"Nat. Rev. Cancer"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"394","DOI":"10.3322\/caac.21492","article-title":"Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries","volume":"68","author":"Bray","year":"2018","journal-title":"CA Cancer J. Clin."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-018-31799-3","article-title":"Novel digital signatures of tissue phenotypes for predicting distant metastasis in colorectal cancer","volume":"8","author":"Sirinukunwattana","year":"2018","journal-title":"Sci. Rep."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Javed, S., Mahmood, A., Fraz, M.M., Koohbanani, N.A., Benes, K., Tsang, Y.W., Hewitt, K., Epstein, D., Snead, D., and Rajpoot, N. (2020). Cellular community detection for tissue phenotyping in colorectal cancer histology images. Med. Image Anal., 63.","DOI":"10.1016\/j.media.2020.101696"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Nearchou, I.P., Soutar, D.A., Ueno, H., Harrison, D.J., Arandjelovic, O., and Caie, P.D. (2021). A comparison of methods for studying the tumor microenvironment\u2019s spatial heterogeneity in digital pathology specimens. J. Pathol. Inform., 12.","DOI":"10.4103\/jpi.jpi_26_20"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1016\/j.neucom.2014.12.012","article-title":"Discrimination between tumour epithelium and stroma via perception-based features","volume":"154","author":"Bianconi","year":"2015","journal-title":"Neurocomputing"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Linder, N., Konsti, J., Turkki, R., Rahtu, E., Lundin, M., Nordling, S., Haglund, C., Ahonen, T., Pietik\u00e4inen, M., and Lundin, J. (2012). Identification of tumor epithelium and stroma in tissue microarrays using texture analysis. Diagn. Pathol., 7.","DOI":"10.1186\/1746-1596-7-22"},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Kather, J.N., Weis, C.A., Bianconi, F., Melchers, S.M., Schad, L.R., Gaiser, T., Marx, A., and Z\u00f6llner, F.G. (2016). Multi-class texture analysis in colorectal cancer histology. Sci. Rep., 6.","DOI":"10.1038\/srep27988"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"9204","DOI":"10.1109\/TIP.2020.3023795","article-title":"Multiplex Cellular Communities in Multi-Gigapixel Colorectal Cancer Histology Images for Tissue Phenotyping","volume":"29","author":"Javed","year":"2020","journal-title":"IEEE Trans. Image Process."},{"key":"ref_14","unstructured":"(2021, March 04). CRCHistoPhenotypes. Available online: https:\/\/warwick.ac.uk\/fac\/cross_fac\/tia\/data\/crchistolabelednucleihe."},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Kather, J.N., Krisam, J., Charoentong, P., Luedde, T., Herpel, E., Weis, C.A., Gaiser, T., Marx, A., Valous, N.A., and Ferber, D. (2019). Predicting survival from colorectal cancer histology slides using deep learning: A retrospective multicenter study. PLoS Med., 16.","DOI":"10.1371\/journal.pmed.1002730"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Kothari, S., Phan, J.H., Young, A.N., and Wang, M.D. (2013). Histological image classification using biologically interpretable shape-based features. BMC Med. Imaging, 13.","DOI":"10.1186\/1471-2342-13-9"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"1502","DOI":"10.1038\/s41379-018-0073-z","article-title":"Using deep convolutional neural networks to identify and classify tumor-associated stroma in diagnostic breast biopsies","volume":"31","author":"Bejnordi","year":"2018","journal-title":"Mod. Pathol."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"1988","DOI":"10.1007\/s10439-018-2095-6","article-title":"Classification of tumor epithelium and stroma by exploiting image features learned by deep convolutional neural networks","volume":"46","author":"Du","year":"2018","journal-title":"Ann. Biomed. Eng."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"874","DOI":"10.1109\/TCBB.2018.2821127","article-title":"Bioimage classification with handcrafted and learned features","volume":"16","author":"Nanni","year":"2018","journal-title":"IEEE\/ACM Trans. Comput. Biol. Bioinform."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Bougourzi, F., Dornaika, F., Mokrani, K., Taleb-Ahmed, A., and Ruichek, Y. (2020). Fusion Transformed Deep and Shallow features (FTDS) for Image-Based Facial Expression Recognition. Expert Syst. Appl., 156.","DOI":"10.1016\/j.eswa.2020.113459"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Wang, S., Yang, D.M., Rong, R., Zhan, X., Fujimoto, J., Liu, H., Minna, J., Wistuba, I.I., Xie, Y., and Xiao, G. (2019). Artificial intelligence in lung cancer pathology image analysis. Cancers, 11.","DOI":"10.3390\/cancers11111673"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.patrec.2021.01.010","article-title":"Deep learning for real-time semantic segmentation: Application in ultrasound imaging","volume":"144","author":"Ouahabi","year":"2021","journal-title":"Pattern Recognit. Lett."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Cascianelli, S., Bello-Cerezo, R., Bianconi, F., Fravolini, M.L., Belal, M., Palumbo, B., and Kather, J.N. (2018, January 20\u201322). Dimensionality reduction strategies for cnn-based classification of histopathological images. Proceedings of the International Conference on Intelligent Interactive Multimedia Systems and Services, Gold Coast, Australia.","DOI":"10.1007\/978-3-319-59480-4_3"},{"key":"ref_24","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1145\/3065386","article-title":"Imagenet classification with deep convolutional neural networks","volume":"60","author":"Krizhevsky","year":"2017","journal-title":"Commun. ACM"},{"key":"ref_26","unstructured":"Iandola, F.N., Han, S., Moskewicz, M.W., Ashraf, K., Dally, W.J., and Keutzer, K. (2016). SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5 MB model size. arXiv."},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., and Rabinovich, A. (2015, January 7\u201312). Going deeper with convolutions. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Boston, MA, USA.","DOI":"10.1109\/CVPR.2015.7298594"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., and Sun, J. (2016, January 27\u201330). Deep residual learning for image recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Las Vegas, NV, USA.","DOI":"10.1109\/CVPR.2016.90"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Elmoataz, A., Lezoray, O., Nouboud, F., and Mammass, D. (2008). Blur Insensitive Texture Classification Using Local Phase Quantization. Image and Signal Processing, Springer. Lecture Notes in Computer Science.","DOI":"10.1007\/978-3-540-69905-7"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"1479","DOI":"10.1049\/iet-ipr.2018.6235","article-title":"Fusion of transformed shallow features for facial expression recognition","volume":"13","author":"Bougourzi","year":"2019","journal-title":"IET Image Process."},{"key":"ref_31","unstructured":"Kannala, J., and Rahtu, E. (2012, January 11\u201315). BSIF: Binarized statistical image features. Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012), Tsukuba, Japan."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-vector networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Xie, S., Girshick, R., Doll\u00e1r, P., Tu, Z., and He, K. (2017, January 21\u201326). Aggregated residual transformations for deep neural networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.634"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2015). Rethinking the Inception Architecture for Computer Vision. arXiv.","DOI":"10.1109\/CVPR.2016.308"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., and Weinberger, K.Q. (2017, January 21\u201326). Densely connected convolutional networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, Honolulu, HI, USA.","DOI":"10.1109\/CVPR.2017.243"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Goyal, P., Girshick, R., He, K., and Doll\u00e1r, P. (2017, January 22\u201329). Focal loss for dense object detection. Proceedings of the IEEE International Conference on Computer Vision, Venice, Italy.","DOI":"10.1109\/ICCV.2017.324"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"012043","DOI":"10.1088\/1757-899X\/428\/1\/012043","article-title":"Age Classification Using Convolutional Neural Networks with the Multi-class Focal Loss","volume":"428","author":"Liu","year":"2018","journal-title":"IOP Conf. Ser. Mater. Sci. Eng."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Bendjoudi, I., Vanderhaegen, F., Hamad, D., and Dornaika, F. (2020). Multi-label, multi-task CNN approach for context-based emotion recognition. Inf. Fusion.","DOI":"10.1016\/j.inffus.2020.11.007"},{"key":"ref_39","unstructured":"Paszke, A., Gross, S., Massa, F., Lerer, A., Bradbury, J., Chanan, G., Killeen, T., Lin, Z., Gimelshein, N., and Antiga, L. (2019, January 8\u201314). Pytorch: An imperative style, high-performance deep learning library. Proceedings of the Advances in Neural Information Processing Systems, Vancouver, BC, Canada."},{"key":"ref_40","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_41","first-page":"1","article-title":"ARA: Accurate, reliable and active histopathological image classification framework with Bayesian deep learning","volume":"9","author":"Zambonelli","year":"2019","journal-title":"Sci. Rep."}],"container-title":["Journal of Imaging"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2313-433X\/7\/3\/51\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T05:35:11Z","timestamp":1760160911000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2313-433X\/7\/3\/51"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,3,9]]},"references-count":41,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2021,3]]}},"alternative-id":["jimaging7030051"],"URL":"https:\/\/doi.org\/10.3390\/jimaging7030051","relation":{},"ISSN":["2313-433X"],"issn-type":[{"value":"2313-433X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,3,9]]}}}