{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T09:50:52Z","timestamp":1777283452801,"version":"3.51.4"},"reference-count":35,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T00:00:00Z","timestamp":1777248000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Oncol."],"abstract":"<jats:p>Colon cancer remains one of the leading causes of cancer-related mortality worldwide, with delayed detection significantly impacting survival rates. The time-consuming nature of traditional histopathological analysis contributes to these diagnostic delays. This study leverages advances in artificial intelligence to improve both the speed and accuracy of colon cancer detection through automated analysis of tissue sample images. We propose a weighted ensemble model comprising four convolutional neural networks namely RegNet X, RegNet Y, Swin B, and VGG11, that enhances prediction accuracy by combining outputs from base models. The prime focus of the research is to devise a recall prioritized ensemble model capable of detecting various types of colon cancers, while also examining the possibility of the model to generalize beyond the training domain. The ensemble was trained using a dataset of 10,000 histopathological images of colon tissues containing both benign and malignant samples. We introduce a novel preprocessing pipeline incorporating histogram equalization and contrast stretching for image enhancement with a motive to improve diagnostic accuracy. Notably, the ensemble model demonstrated superior recall metrics compared to traditional ML approaches\u2014a critical measurement in healthcare applications. The ensemble model proposed has a test accuracy of 99% and a recall of almost 100%. Further, the proposed model is also tested for lung cancer to validate its generalizability. The experimental results obtained establish the validation of proposed model in terms of generalizability, demonstrating strong performance.<\/jats:p>","DOI":"10.3389\/fonc.2026.1674606","type":"journal-article","created":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T09:12:37Z","timestamp":1777281157000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Ensemble CNN for colon cancer detection using histopathological image"],"prefix":"10.3389","volume":"16","author":[{"given":"Akshay","family":"Kulkarni","sequence":"first","affiliation":[{"name":"Department of Information Technology, Dwarkadas J. Sanghvi College of Engineering","place":["Mumbai, India"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Amit","family":"Upadhyay","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Dwarkadas J. Sanghvi College of Engineering","place":["Mumbai, India"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Monika","family":"Mangla","sequence":"additional","affiliation":[{"name":"Department of Information Technology, Dwarkadas J. Sanghvi College of Engineering","place":["Mumbai, India"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Swati","family":"Aggarwal","sequence":"additional","affiliation":[{"name":"Department of Logistics, Molde University College","place":["Molde, Norway"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2026,4,27]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"664","DOI":"10.1186\/s12885-016-2717-z","article-title":"Effect of diagnostic delay on survival in patients with colorectal cancer: a retrospective cohort study","volume":"16","author":"Pita-Fern\u00e1ndez","year":"2016","journal-title":"BMC Cancer"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.7326\/m20-2928","article-title":"Surprise billing for colonoscopy: the scope of the problem","volume":"174","author":"Scheiman","year":"2021","journal-title":"Ann Internal Med"},{"key":"B3","doi-asserted-by":"publisher","first-page":"18","DOI":"10.54216\/jcim.060102","article-title":"Image classification based on cnn: a survey","volume":"6","author":"Elngar","year":"2021","journal-title":"J Cybersecurity Inf Manage"},{"key":"B4","doi-asserted-by":"publisher","first-page":"5979","DOI":"10.1038\/s41598-022-09954-8","article-title":"On evaluation metrics for medical applications of artificial intelligence","volume":"12","author":"Hicks","year":"2022","journal-title":"Sci Rep"},{"key":"B5","doi-asserted-by":"publisher","DOI":"10.3322\/caac.21660","article-title":"Global cancer statistics 2020: Globocan estimates of incidence and mortality worldwide for 36 cancers in 185 countries","volume":"71","author":"Sung","year":"2021","journal-title":"CA: A Cancer J For Clin"},{"key":"B6","doi-asserted-by":"crossref","DOI":"10.1109\/CSNT51715.2021.9509670","article-title":"Medical image enhancement with brightness preserving based on local contrast stretching and global dynamic histogram equalization","author":"Panse","year":"2021"},{"key":"B7","first-page":"1","article-title":"Performance improvement of facial expression recognition deep neural network models using histogram equalization and contrast stretching","author":"Mathew","year":"2021"},{"key":"B8","doi-asserted-by":"publisher","DOI":"10.1002\/widm.1439","article-title":"Deep learning in histopathology: A review","volume":"12","author":"Banerji","year":"2022","journal-title":"Wiley Interdiscip Reviews: Data Min Knowledge Discov"},{"key":"B9","doi-asserted-by":"publisher","first-page":"4200","DOI":"10.1038\/s41598-024-83466-5","article-title":"Advances in colorectal cancer diagnosis using optimal deep feature fusion approach on biomedical images","volume":"15","author":"Alotaibi","year":"2025","journal-title":"Sci Rep"},{"key":"B10","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1912.12142","article-title":"Lung and colon cancer histopathological image dataset (lc25000)","author":"Borkowski","year":"2019","journal-title":"arXiv preprint arXiv:1912.12142"},{"key":"B11","doi-asserted-by":"publisher","DOI":"10.1016\/j.heliyon.2024.e30625","article-title":"Colon and lung cancer classification from multi-modal images using resilient and efficient neural network architectures","volume":"10","author":"Uddin","year":"2024","journal-title":"Heliyon"},{"key":"B12","doi-asserted-by":"crossref","DOI":"10.1109\/ICECE51594.2020.9353044","article-title":"An improved faster R-CNN for colorectal cancer cell detection","author":"Yu","year":"2020"},{"key":"B13","doi-asserted-by":"publisher","DOI":"10.1177\/20552076251361603","article-title":"Mspo: A machine learning hyperparameter optimization method for enhanced breast cancer image classification","volume":"11","author":"Li","year":"2025","journal-title":"Digital Health"},{"key":"B14","doi-asserted-by":"publisher","DOI":"10.1177\/20552076241249661","article-title":"Optimizing lung cancer classification through hyperparameter tuning","volume":"10","author":"Nabeel","year":"2024","journal-title":"Digital Health"},{"key":"B15","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2024.106501","article-title":"Optimizing diabetic retinopathy detection with inception-v4 and dynamic version of snow leopard optimization algorithm","volume":"96","author":"Yang","year":"2024","journal-title":"BioMed Signal Process Control"},{"key":"B16","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2021.104730","article-title":"Deep learning for colon cancer histopathological images analysis","volume":"136","author":"Hamida","year":"2021","journal-title":"Comput Biol Med"},{"key":"B17","doi-asserted-by":"publisher","DOI":"10.1002\/ima.22623","article-title":"Classification with respect to colon adenocarcinoma and colon benign tissue of colon histopathological images with a new CNN model: Ma colonnet","volume":"32","author":"Yildirim","year":"2022","journal-title":"Int J Imaging Syst Technol"},{"key":"B18","doi-asserted-by":"publisher","DOI":"10.1016\/j.compbiomed.2022.105680","article-title":"A convolution neural network with multi-level convolutional and attention learning for classification of cancer grades and tissue structures in colon histopathological images","volume":"147","author":"Dabass","year":"2022","journal-title":"Comput Biol Med"},{"key":"B19","article-title":"Deep learning-based histopathological analysis for colon cancer diagnosis: A comparative study of cnn and transformer models with image preprocessing techniques","volume-title":"Lecture Notes in Networks and Systems","author":"Karthik","year":"2024"},{"key":"B20","doi-asserted-by":"publisher","DOI":"10.1177\/20552076231172632","article-title":"Intelligent lung cancer mri prediction analysis based on cluster prominence and posterior probabilities utilizing intelligent bayesian methods on extracted gray-level co-occurrence (glcm) features","volume":"9","author":"Yang","year":"2023","journal-title":"DIGITAL Health"},{"key":"B21","doi-asserted-by":"crossref","DOI":"10.1109\/ICAC3N56670.2022.10074383","article-title":"Explainable ai and deep learning techniques for colon cancer detection","author":"Sholapur","year":"2022"},{"key":"B22","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1371\/journal.pone.0317306","article-title":"Color correction methods for underwater image enhancement: A systematic literature review","volume":"20","author":"Lai","year":"2025","journal-title":"PloS One"},{"key":"B23","doi-asserted-by":"publisher","first-page":"18131","DOI":"10.1038\/s41598-025-02942-8","article-title":"Mhf-uie: A multi-task hybrid fusion method for real-world underwater image enhancement","volume":"15","author":"Xu","year":"2025","journal-title":"Sci Rep"},{"key":"B24","first-page":"36","article-title":"Enhancing colorectal cancer histological image classification using transfer learning and resnet50 cnn model","author":"Peng","year":"2023"},{"key":"B25","doi-asserted-by":"publisher","first-page":"8823","DOI":"10.1038\/s41598-023-35431-x","article-title":"Ensemble-based multi-tissue classification approach of colorectal cancer histology images using a novel hybrid deep learning framework","volume":"13","author":"Khazaee Fadafen","year":"2023","journal-title":"Sci Rep"},{"key":"B26","doi-asserted-by":"publisher","DOI":"10.1007\/s10916-023-01994-5","article-title":"A federated learning approach to tumor detection in colon histology images","volume":"47","author":"Gunesli","year":"2023","journal-title":"J Med Syst"},{"key":"B27","doi-asserted-by":"publisher","DOI":"10.3233\/XST-221301","article-title":"Lung cancer histopathological image classification using wavelets and alexnet","volume":"31","author":"Sethy","year":"2022","journal-title":"J X-Ray Sci Technol"},{"key":"B28","doi-asserted-by":"publisher","first-page":"1","DOI":"10.6703\/ijase.202303_20(1).006","article-title":"Diagnosis of lung and colon cancer based on clinical pathology images using convolutional neural network and clahe framework","volume":"20","author":"Hadiyoso","year":"2023","journal-title":"Int J Appl Sci Eng"},{"key":"B29","doi-asserted-by":"crossref","DOI":"10.1063\/5.0114327","article-title":"Lung and colon cancer detection with convolutional neural networks on histopathological images","author":"Wahid","year":"2023"},{"key":"B30","doi-asserted-by":"publisher","DOI":"10.3390\/biomimetics8040370","article-title":"A novel heteromorphous convolutional neural network for automated assessment of tumors in colon and lung histopathology images","volume":"8","author":"Iqbal","year":"2023","journal-title":"Biomimetics"},{"key":"B31","doi-asserted-by":"publisher","DOI":"10.3390\/cancers15133300","article-title":"Al-biruni earth radius optimization with transfer learning based histopathological image analysis for lung and colon cancer detection","volume":"15","author":"AlGhamdi","year":"2023","journal-title":"Cancers"},{"key":"B32","doi-asserted-by":"publisher","first-page":"4597445","DOI":"10.1155\/2023\/4597445","article-title":"Using deep learning with bayesian\u2013gaussian inspired convolutional neural architectural search for cancer recognition and classification from histopathological image frames","volume":"2023","author":"Stephen","year":"2023","journal-title":"J Healthcare Eng"},{"key":"B33","doi-asserted-by":"publisher","DOI":"10.1016\/j.bspc.2022.103596","article-title":"An empirical study of handcrafted and dense feature extraction techniques for lung and colon cancer classification from histopathological images","volume":"75","author":"Kumar","year":"2022","journal-title":"BioMed Signal Process Control"},{"key":"B34","article-title":"Novel architecture for data shuffling using fisher yates shuffle algorithm","volume":"1","author":"Aishwarya","year":"2015","journal-title":"Int J Sci Res Science Eng Technol"},{"key":"B35","doi-asserted-by":"publisher","DOI":"10.1007\/s11042-022-13419-5","article-title":"Breast cancer classification using snapshot ensemble deep learning model and t-distributed stochastic neighbor embedding","volume":"82","author":"Sharma","year":"2023","journal-title":"Multimedia Tools Appl"}],"container-title":["Frontiers in Oncology"],"original-title":[],"link":[{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fonc.2026.1674606\/full","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,27]],"date-time":"2026-04-27T09:12:45Z","timestamp":1777281165000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.frontiersin.org\/articles\/10.3389\/fonc.2026.1674606\/full"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,4,27]]},"references-count":35,"alternative-id":["10.3389\/fonc.2026.1674606"],"URL":"https:\/\/doi.org\/10.3389\/fonc.2026.1674606","relation":{},"ISSN":["2234-943X"],"issn-type":[{"value":"2234-943X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,4,27]]},"article-number":"1674606"}}